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ISBN 978-951-42-9554-6 (Paperback)ISBN 978-951-42-9555-3 (PDF)ISSN 0355-3221 (Print)ISSN 1796-2234 (Online)
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Eija Suorsa
OULU 2011
D 1118
Eija Suorsa
ASSESSMENT OF HEART RATE VARIABILITY AS AN INDICATOR OF CARDIOVASCULAR AUTONOMIC DYSREGULATION IN SUBJECTS WITH CHRONIC EPILEPSY
UNIVERSITY OF OULU,FACULTY OF MEDICINE,INSTITUTE OF CLINICAL MEDICINE,DEPARTMENT OF NEUROLOGY
A C T A U N I V E R S I T A T I S O U L U E N S I SD M e d i c a 1 1 1 8
EIJA SUORSA
ASSESSMENT OF HEART RATE VARIABILITY AS AN INDICATOR OF CARDIOVASCULAR AUTONOMIC DYSREGULATION IN SUBJECTS WITH CHRONIC EPILEPSY
Academic dissertation to be presented with the assent ofthe Faculty of Medicine of the University of Oulu forpublic defence in Auditorium 8 of Oulu UniversityHospital, on 11 November 2011, at 12 noon
UNIVERSITY OF OULU, OULU 2011
Copyright © 2011Acta Univ. Oul. D 1118, 2011
Supervised byDocent Jouko IsojärviDocent Juha Korpelainen
Reviewed byProfessor Torbjörn TomsonDocent Aarne Ylinen
ISBN 978-951-42-9554-6 (Paperback)ISBN 978-951-42-9555-3 (PDF)
ISSN 0355-3221 (Printed)ISSN 1796-2234 (Online)
Cover DesignRaimo Ahonen
JUVENES PRINTTAMPERE 2011
Suorsa, Eija, Assessment of heart rate variability as an indicator of cardiovascularautonomic dysregulation in subjects with chronic epilepsy. University of Oulu, Faculty of Medicine, Institute of Clinical Medicine, Department ofNeurology, P.O. Box 5000, FI-90014 University of Oulu, FinlandActa Univ. Oul. D 1118, 2011Oulu, Finland
AbstractAutonomic dysfunction in epilepsy is widely recognized. Both partial and generalized epilepsiesaffect autonomic functions during interictal, ictal and postictal states. Interestingly, there isincreasing evidence of interictal autonomic nervous system dysfunction as evidenced by reducedheart rate (HR) variability in patients with epilepsy. Reduced HR variation has also been detectedin many other chronic diseases and it has been shown to be associated with unfavourable prognosiswith an increased risk of mortality in various heart diseases. Recently, more attention has also beenpaid to possible association of decreased HR variability with sudden unexpected death in epilepsy(SUDEP). However, the clinical significance of the observed changes in cardiovascular regulationin patients with epilepsy is still poorly outlined and there are no long-term studies about changesin HR variation in relation to epilepsy.
This study was designed to evaluate long-term changes in autonomic cardiovascular regulationin patients with temporal lobe epilepsy (TLE) and also to evaluate HR variation during vagusnerve stimulation (VNS) treatment in patients with refractory epilepsy, using 24-hour ambulatoryECG recordings. Special attention was paid to changes in HR variation and to circadian HRfluctuation over time.
The results of this study show that autonomic cardiovascular regulation is affected both inpatients with well-controlled TLE and in patients with refractory TLE, and that the cardiovasculardysregulation also presents itself with changes in circadian HR variability, with more pronouncedalterations observed during the night time. HR variability was also found to decrease progressivelywith time in patients with chronic refractory TLE with uncontrolled seizures. VNS treatment wasnot observed to alter HR variation.
Keywords: autonomic nervous system, heart rate variation, sudden unexpected death inepilepsy, temporal lobe epilepsy, vagus nerve stimulation
Suorsa, Eija, Sydämen sykevaihtelu kroonisessa epilepsiassa. Oulun yliopisto, Lääketieteellinen tiedekunta, Kliinisen lääketieteen laitos, Neurologia, PL5000, 90014 Oulun yliopistoActa Univ. Oul. D 1118, 2011Oulu
TiivistelmäEpilepsiapotilailla esiintyy autonomisen hermoston toiminnan häiriöitä. Näitä häiriöitä voidaantodeta epilepsiakohtausten aikana, heti kohtausten jälkeen ja kohtausten välillä sekä paikallisal-kuisissa että yleistyneissä epilepsioissa. Viimeaikaisissa tutkimuksissa on osoitettu kardiovasku-laarisen säätelyjärjestelmän häiriöiden voivan ilmentyä alentuneena sydämen sykevaihtelunaepilepsiakohtausten väliaikoina. Sydänsairauksien yhteydessä sykevaihtelun vähenemisen onosoitettu liittyvän kohonneeseen kuolemanriskiin. Epilepsiapotilailla alentuneen sydämen syke-vaihtelun on epäilty liittyvän epilepsiapotilailla ilmenevien odottamattomien ja ilman selkeääsyytä tapahtuvien äkkikuolemien (SUDEP) lisääntyneeseen riskiin. Kertyneestä tiedosta huoli-matta alentuneen sykevaihtelun kliininen merkitys epilepsiapotilailla on edelleen epäselvä. Pit-käaikaisseurantatutkimuksia sydämen sykevaihtelun muutoksista epilepsiapotilailla ei ole jul-kaistu.
Tämän tutkimuksen tarkoituksena oli selvittää ohimolohkoepilepsiaan liittyviä pitkäaikaisiainteriktaalisia (kohtausten välillä esiintyviä) kardiovaskulaarisia ilmentymiä. Lisäksi haluttiintutkia vaikeahoitoisessa epilepsiassa käytetyn hoitomuodon, vagushermostimulaation, mahdolli-sia vaikutuksia sydämen toimintaan. Erityisesti haluttiin analysoida sykevaihtelun vuorokausi-rytmiä.
Tulokset osoittavat autonomisen hermoston kardiovaskulaarisen säätelyjärjestelmän toimin-nan olevan häiriintyneen sekä vaikeahoitoisilla että hyvähoitoisilla ohimolohkoalkuista epilepsi-aa sairastavilla potilailla. Sydämen sykevariaatio on alentunut erityisesti yöaikaan. Lisäksi sydä-men sykevaihtelu alenee pitkäaikaisseurannassa vaikeahoitoista epilepsiaa sairastavilla potilail-la, joilla ilmenee toistuvia epileptisiä kohtauksia. Vagusstimulaatio ei aiheuttanut muutoksiasyketaajuuden vaihteluun.
Asiasanat: autonominen hermosto, ohimolohko epilepsia, sydämen sykevaihtelu,vagushermo stimulaattori, äkkikuolema
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Acknowledgements
The research for this thesis was carried out at the Department of Neurology, at the
University of Oulu, during the years 1999–2011.
I wish to express my warmest thanks to my supervisor, Docent Jouko
Isojärvi, for introducing me to the world of epilepsy science with such expertise.
Your positive and enthusiastic attitude has given me inspiration and guided me
over the difficult times as well. I also want to thank my supervisor Docent Juha
Korpelainen, who provided his practical advice and shared his scientific
knowledge. You always had time for discussions and your tireless encouragement
made it possible to finish this work. Thanks to my supervisor’s encouragement,
logical thinking and supportive responsibility distribution, I have found the world
of science fascinating and challenging. It has been a privilege to work with you.
I wish to express my gratitude to Professor Vilho V. Myllylä, for his guidance
and sympathetic support for my study. His broad experience of science has
created an inspiring atmosphere to work in. I am also very grateful to Professor
Matti Hillbom and Professor Kari Majamaa for providing research facilities for
scientific work.
I wish to thank Professor Heikki Huikuri for his excellence guidance in the
field of heart rate variability analysis, and Pirkko Huikuri for her valuable
technical assistance in heart rate variability data processing. I also wish to thank
Professor Esa Heikkinen for his expertise in the field of neurosurgery with vagus
nerve stimulation. I also thank Docent Kyösti Sotaniemi for his kind
encouragement and interest in my research work.
I wish to acknowledge most sincerely Professor Torbjörn Tomson and Docent
Aarne Ylinen for their expertise and valuable comments during the preparation of
the final manuscript for this thesis. I also feel honoured that Professor Tapani
Keränen kindly agreed to serve as my opponent. I express my appreciation to
Anna Vuolteenaho for the careful revision of the English language of the
manuscript.
I owe my thanks to the entire staff of the Departments of Neurology and
Division of Cardiology for their excellent co-operation through the years of this
study. Special thanks are due to Ilona Huovinen for her friendly secretarial
assistance throughout the study. I would also like to thank Risto Bloigu for his
expertise with statistical analyses. I wish to express my sincere gratitude to the
patients and their families who made this work possible.
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I warmly thank my colleagues in epilepsy research, Johanna Rättyä, Hanna
Ansakorpi, Virpi Pylvänen, Eeva Löfgren, Katja Luoma, Kirsi Mikkonen and
Usko Huuskonen, for their interest in my project as well as for your friendship
and inspiring conversation. I warmly thank Jaana Huttula and Paula Kelhä for
their friendship and assistance.
It is my pleasure to thank Peter Baumann for introducing me to the world of
clinical neurology. It was such a pleasure to work with you.
I thank all my dear and cheerful friends, who have made life more interesting
and enjoyable for me. Henna Mustaniemi, Milla Riski and Anne Mäntyniemi,
deserves my loving thanks for friendship and laughs. You have a special place in
my heart! My parents-in-law, Sinikka and Esko Suorsa are warmly thanked for
their endless belief that I would finish this work, and also for practical help with
the care of our children.
I thank my sister Piia Pitkänen for your love, support and laughter that have
kept me going. Your own projects, Ida and Oskari, are my sunshine. I owe my
greatest gratitude to my wonderful parents Salme and Veikko Ronkainen for
providing me with a supportive family and for advising me when needed. Your
enormous encouragement and belief in me have made lots of things happen.
Finally, my dearest thanks are expressed to my life companion Ville Suorsa.
Not only for your work for this thesis but also your patience and love. We have
been blessed with two special girls, Ella and Anni. Love you.
This research project was supported by grants from the Epilepsy Research
Foundation, the Research Foundation of Orion Corporation, the Centre for Arctic
Medicine and the Oulu Medical Research Foundation. All these are warmly
acknowledged.
Oulu, September 2011 Eija Suorsa
11
Abbreviations
α short-term scaling exponent
β slope of the power-law relationship
AED antiepileptic drug
ANS autonomic nervous system
ApEn approximate entropy
CBZ carbamazepine
CNS central nervous system
CT computerized tomography
ECG electrocardiography
EEG electroencephalography
GABA gamma-amino butyric acid
GBP gabapentin
HF high frequency
HR heart rate
LF low frequency
LEV levetiracetam
LTG lamotrigine
MRI magnetic resonance imaging
NTS nucleus tractus solitarius
OXC oxcarbazepine
PHT phenytoin
RR interval R-peak-to-R-peak interval
SD1 instantaneous beat-to-beat RR interval variability
SD2 long-term continuous RR interval variability
SDNN standard deviation of all RR intervals
SUDEP sudden unexpected death in epilepsy
TGB tiagabine
TLE temporal lobe epilepsy
TPM topiramate
VGB vigabatrin
VLF very low frequency
VNS vagus nerve stimulation/stimulator
VPA valproate
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List of original articles
This thesis is based on the following publications, which are cited in the text by
their Roman numerals:
I Ronkainen E, Ansakorpi H, Huikuri HV, Myllylä VV, Isojärvi JIT & Korpelainen JT (2005) Suppressed circadian heart rate dynamics in temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 76(10): 1382–1386.
II Suorsa E, Korpelainen JT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Isojärvi JIT (2011) Heart rate dynamics in temporal lobe epilepsy – a long term follow-up study. Epilepsy Research 93(1): 80–83.
III Suorsa E, Isojärvi JIT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Korpelainen JT (2011) Long-term changes in circadian heart rate variability in patients with temporal lobe epilepsy. Manuscript
IV Ronkainen E, Korpelainen JT, Heikkinen E, Myllylä VV, Huikuri HV & Isojärvi JIT (2006) Cardiac autonomic control in patients with refractory epilepsy before and during vagus nerve stimulation treatment – a one year follow-up study. Epilepsia 47(3): 556–562.
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Contents
Abstract
Tiivistelmä
Acknowledgements 7 Abbreviations 11 List of original articles 13 Contents 15 1 Introduction 17 2 Review of the literature 19
2.1 General aspects of epilepsy ..................................................................... 19 2.1.1 Definition...................................................................................... 19 2.1.2 Epidemiology ............................................................................... 19 2.1.3 Aetiology ...................................................................................... 20 2.1.4 Classification ................................................................................ 20 2.1.5 Diagnosis ...................................................................................... 24 2.1.6 Prognosis ...................................................................................... 25
2.2 Temporal lobe epilepsy ........................................................................... 25 2.3 Treatment of epilepsy .............................................................................. 26
2.3.1 Antiepileptic drugs ....................................................................... 26 2.3.2 Surgery ......................................................................................... 31 2.3.3 Vagus nerve stimulation ............................................................... 32
2.4 Autonomic nervous system ..................................................................... 34 2.4.1 Anatomy of the autonomic nervous system ................................. 34 2.4.2 Cardiovascular regulation ............................................................. 35
2.5 Heart rate variability and its clinical implications .................................. 39 2.5.1 Physiological background of heart rate variability and
heart rate dynamics ....................................................................... 39 2.5.2 Factors affecting heart rate variability .......................................... 41 2.5.3 Heart rate variability in pathological conditions .......................... 42
2.6 Epilepsy and autonomic cardiovascular dysregulation ........................... 43 2.6.1 Ictal autonomic dysfunction ......................................................... 43 2.6.2 Interictal heart rate variation ........................................................ 44 2.6.3 Circadian heart rate variation ....................................................... 45 2.6.4 Effect of vagus nerve stimulation on cardiovascular
autonomic function ....................................................................... 46 2.7 Sudden unexpected death in epilepsy ..................................................... 47
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2.7.1 Definition ...................................................................................... 47 2.7.2 Epidemiology ............................................................................... 47 2.7.3 Aetiology ...................................................................................... 48
3 Aims of the study 51 4 Subjects and methods 53
4.1 Subjects ................................................................................................... 53 4.2 Methods ................................................................................................... 56
4.2.1 Clinical examination (Studies I-IV) .............................................. 56 4.2.2 Adjustment and use of vagus nerve stimulator (Study IV) ........... 56 4.2.3 Analysis of heart rate behaviour (Studies I-IV) ............................ 57 4.2.4 Statistical analysis ......................................................................... 59
5 Results 61 5.1 Clinical evaluation of autonomic nervous system function .................... 61 5.2 Cardiac regulation in temporal lobe epilepsy .......................................... 61
5.2.1 Long-term heart rate dynamics (Study II) .................................... 61 5.2.2 Circadian heart rate variation (Study I) ........................................ 62 5.2.3 Long-term changes in circadian heart rate variation (Study
III) ................................................................................................. 65 5.3 Effect of vagus nerve stimulation on heart rate dynamics (Study
IV) ........................................................................................................... 68 6 Discussion 71
6.1 General aspects........................................................................................ 71 6.2 Clinical findings of autonomic nervous system function in
patients with epilepsy .............................................................................. 72 6.3 Cardiac regulation in temporal lobe epilepsy .......................................... 72
6.3.1 Long-term heart rate dynamics ..................................................... 72 6.3.2 Circadian heart rate variation ....................................................... 73 6.3.3 Long-term changes in circadian heart rate variation .................... 75 6.3.4 Effect of antiepileptic medication on heart rate variation ............. 76
6.4 Effect of vagus nerve stimulation on heart rate dynamics....................... 76 6.5 Methodological considerations ............................................................... 77
7 Conclusions 79 References 81 Original publications 101
17
1 Introduction
Epilepsy is a descriptive term for a large group of anatomical and functional
disorders of the brain that are characterized by repeated seizures. Epilepsy is one
of the most common serious brain disorders. Throughout history, epilepsy has
been associated with superstition and stigma, but over the past decades scientists
have made significant advances in the understanding and treatment of this
disorder. Simultaneously, public knowledge of this disorder has improved, and
attitudes have slowly changed towards its acceptance.
The prevalence of active epilepsy is estimated at 3–8 per 1,000 of the
population according to European studies (Keränen et al. 1989, Olafsson &
Hauser 1999, Rocca et al. 2001). The treatment of epilepsy is mainly based on
drug treatment. However, for those who do not achieve seizure freedom despite
adequate antiepileptic drug (AED) treatment epilepsy surgery may be a
therapeutic alternative. With appropriate treatment, 70–75% of patients with
epilepsy become seizure-free, but the remainder will be resistant to treatment and
classified as patients with refractory epilepsy (Sander & Shorvon 1996, Cockerell
et al. 1997, Kwan & Brodie 2000, Kwan & Sander 2004). Electrical stimulation
of the vagus nerve is a treatment for patients with refractory epilepsy who are
unsuitable candidates for resective surgery or who have experienced insufficient
benefit from such treatment (Uthman et al. 1993, Ben-Menachem 2002). Despite
appropriate treatment, patients with epilepsy have mortality rates that are 2–3
times higher compared to general population (Olafsson et al. 1998, Tomson
2000).
Epilepsy may affect autonomic nervous system (ANS) function during
interictal (between seizures), ictal (during seizures) and postictal (after seizures)
states. Cardiac function may be altered in patients with epilepsy as a result of
autonomic dysfunction at several different levels, including central and peripheral
ANS pathways, and also at the level of the heart. It is not clear whether the
observed imbalance of the sympathetic and parasympathetic input to the heart in
patients with epilepsy is due to epilepsy per se or whether other factors, such as
medication or its withdrawal, may play a role as well (Isojärvi et al. 1998,
Tomson et al. 1998, Hennessy et al. 2001, Ansakorpi et al. 2002).
Traditional time and frequency domain measures of heart rate (HR)
variability along with the newer methods based on fractal and complexity scaling
of RR interval variability have both been used as non-invasive tools for assessing
autonomic cardiovascular regulation (Task Force 1996). Using conventional
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short- and long-term ECG recordings, previous studies have reported diminished
interictal HR variability in patients with epilepsy, mainly temporal lobe epilepsy
(TLE), but it has remained unclear whether the observed reduction in
cardiovascular responses is due to the epileptic process itself or to the AEDs
(Frysinger et al. 1993, Isojärvi et al. 1998, Tomson et al. 1998, Ansakorpi et al.
2000, Ansakorpi et al. 2002). On the other hand, low HR variability has also been
reported in a number of other pathophysiologic conditions (Bernardi et al. 1992,
Huikuri et al. 1994, Korpelainen et al. 1997), and has been shown to be a marker
of an increased risk of mortality in patients with these conditions (Kleiger et al.
1987, Huikuri et al. 1994, Malliani et al. 1994, Barron & Viskin 1998).
Patients with epilepsy are at increased risk for SUDEP, and a large body of
data has defined different risk factors for SUDEP, e.g. youth, polytherapy with
AEDs, lack of compliance with treatment and poor seizure control (Devinsky et
al. 1994, Nilsson et al. 1999, Opeskin et al. 2000, Tomson et al. 2008). However,
otherwise healthy, compliant patients may also die unexpectedly (Earnest et al.
1992, Nashef et al. 1998). Recently, more attention has been paid to possible
association between altered cardiovascular function and SUDEP. It has been
suggested that reduced HR variability may play a role in the pathophysiology of
sudden unexpected death in epilepsy (SUDEP) in patients with chronic epilepsy
(Tomson et al. 1998). However, according to one recent study no HR variability
parameter was associated with SUDEP, suggesting that HR variability parameters
are not clear-cut predictors for SUDEP (Surges et al. 2009a).
There are no previous longitudinal studies on changes in autonomic
cardiovascular regulation in patients with TLE over time. There is also limited
information on the circadian HR variation in this patient group. Furthermore,
there are only few published short-term studies on the effects of VNS treatment
on cardiovascular regulation despite the close interaction between VNS and the
centres controlling cardiovascular regulation.
The present study was designed to evaluate long-term cardiovascular
autonomic regulation in patients with TLE and in patients with VNS treatment by
using 24-hour ECG recordings.
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2 Review of the literature
2.1 General aspects of epilepsy
2.1.1 Definition
Epilepsy is the name of a brain disorder characterized predominantly by recurrent
and unpredictable interruptions of normal brain function, called epileptic seizures.
Epileptic seizures can affect sensory, motor, and autonomic function, which are
characterized by various clinical manifestations such as decrease of
consciousness, abnormal sensory phenomena, increased autonomic activity and
involuntary movements. Epilepsy is not a singular disease entity but a dynamic
process, which reflects complex functional changes occurring in the anatomy and
physiology of the brain in the presence of environmental and genetic factors.
(Waltimo 1983, Fisher et al. 2005)
2.1.2 Epidemiology
Epilepsy is the most common chronic disorder of the central nervous system
(CNS). Numerous studies have been published on the epidemiology of epilepsy.
However, the study designs and the definition of epilepsy and seizure types differ
from one study to another, which makes comparison between different studies
difficult.
Prospective, population-based studies indicate that in general population
there is an 8–10% lifetime risk of one seizure (Hauser et al. 1990) and a 3%
chance of epilepsy (Hauser et al. 1993). The incidence of epilepsy is 24–53
/100,000 person years in developed countries (Keränen et al. 1989, Olafsson et al.
1996, Zarrelli et al. 1999, MacDonald et al. 2000), whereas in developing
countries it is considered to be higher. The incidence is high in childhood and
increases again in elderly people (Sillanpää 1973, Keränen et al. 1989, Hauser et
al. 1993, Forsgren et al. 1996, Olafsson et al. 1996, Beilmann et al. 1999).
Interestingly, a recent population-based study found that the incidence of epilepsy
in Finland has declined significantly in both children and adults with a concurrent
increase in incidence among the elderly (Sillanpää et al. 2006), but the reasons for
the changes in incidence remained unclear.
The prevalence of active epilepsy, often defined as patients with epilepsy who
have had at least one seizure during the last 5 years, is 3.3–7.8 per 1000
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inhabitants in studies performed in European countries (Granieri et al. 1983,
Keränen et al. 1989, Olafsson & Hauser 1999, Rocca et al. 2001). In most
previous studies the prevalence of epilepsy has been higher in males than females
(Granieri et al. 1983, Hauser 1997, Olafsson & Hauser 1999, Rocca et al. 2001).
However, the prevalence difference between genders has rarely been shown to be
statistically significant (Granieri et al. 1983, Keränen et al. 1989). According to
the Finnish Social Insurance Institution, 58,594 patients out of a total population
of approximately 5 million received reimbursement for antiepileptic medication
in Finland in 2009. Approximately 9,000 of these patients suffer from medically
intractable epilepsy. (Social Insurance Institution 2006)
2.1.3 Aetiology
Almost any cerebral pathology may be associated with epilepsy. In the adult
population the cause of epilepsy is unknown in the majority of patients (Beghi
2004). The most common causes of epilepsy are cerebrovascular diseases, head
trauma, intracranial haemorrhages, cerebral tumours and neurodegenerative
diseases. (Sander et al. 1990, Forsgren et al. 1996, Olafsson et al. 1996, Oun et
al. 2003). In children metabolic defects, congenital malformations, infections and
genetic diseases are among common aetiologies (Beghi 2004). It seems that
almost everyone may experience a seizure in a particular set of circumstances, but
some people seem to have a lower seizure threshold than others.
2.1.4 Classification
Classification of epileptic seizures
Epileptic seizures can be classified in several different ways. Their electroclinical
features identify them as either partial or generalized. Partial seizures start in a
circumscribed set of nerve cells (the “epileptic focus”) in one hemisphere of the
brain and spread from there. Generalized seizures involve both sides of the brain
from the onset, although they may sometimes involve only a small part of the two
hemispheres in a symmetrical manner. (ILAE 1981) Table 1 presents 1981 ILAE
classification of seizures, which was used in the present thesis. However, new
terminology and classification has been established since the start of this research
(ILAE 2005–2009) (Berg et al. 2010).
21
According to new classification of epilepsies, the diagnostic scheme provides
the basis for a standardized description of individual patients, and consists of five
levels. The levels are organized to facilitate a logical clinical approach to the
development of hypotheses necessary to determine the diagnostic studies that
should be performed in individual patients. The levels are 1) ictal phenomenology
2) epileptic seizure type/types 3) epileptic syndrome 4) aetiology and 5) the
degree of impairment caused by the epileptic condition.
Table 1. International classification of seizures (Commission on Classification and
Terminology, ILAE, 1981).
Class Classification
I Partial (focal) seizures
A Simple partial seizures (consciousness not impaired)
1. With motor signs
Focal motor without march
Focal motor with march (Jacksonian)
Versive
Postural
Phonatory (vocalization or arrest of speech)
2. With somatory or special-sensory symptoms (simple hallucinations, e.g. tingling, light
flashes, buzzing)
Somatosensory
Visual
Auditory
Olfactory
Gustatory
Vertiginous
3. With autonomic symptoms or signs (including epigastric sensation, pallor, sweating,
flushing, piloerection, and pupillary dilatation)
4. With psychic symptoms (disturbances of higher cerebral functions); these symptoms rarely
occur without impairment of consciousness and are much more commonly experienced as
complex partial seizures
Dysphasic
Dynamic (e.g. déjà vu)
Cognitive (e.g. dreamy states, distortions of time sense)
Affective (fear, anger, etc.)
Illusions (e.g. macropsia)
Structured hallucinations (e.g. music, scenes)
B. Complex partial seizures (with impairment of consciousness; may sometimes begin with
simple symptomatology)
1. Simple partial onset followed by impairment of consciousness
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Class Classification
With simple partial features (A.1. - A.4.) followed by impaired consciousness
With automatisms
2. With impairment of consciousness at onset
With impairment of consciousness only
With automatisms
C. Partial seizures evolving to secondary generalized seizures (may be generalized tonic-
clonic, tonic, or clonic)
1. Simple partial seizures (A) evolving to generalized seizures
2. Complex partial seizures (B) evolving to generalized seizures
3. Simple partial seizures evolving to complex partial seizures evolving to generalized seizures
II Generalized seizures (convulsive or non-convulsive)
A.
1. Absence seizures
Impairment of consciousness only
With mild clonic components
With atonic components
With tonic components
With automatisms
With autonomic components
2. Atypical absence
May have:
Changes in tone that are more pronounced than in A.1.
Onset and/or cessation that is not abrupt
B. Myoclonic seizures
Myoclonic jerks (single or multiple)
C. Clonic seizures
D. Tonic seizures
E. Tonic-clonic seizures
F. Atonic seizures
III Unclassified seizures
Includes all seizures that cannot be classified because of inadequate or incomplete data.
Classification of epilepsies and epileptic syndromes
Epilepsies and epileptic syndromes have been classified according to aetiology
and anatomic origin of seizures. Symptomatic epilepsies are due to a recognizable
insult to the brain (e.g. resulting from a malformation, trauma, or tumour). In
idiopathic epilepsies, a known cause cannot be identified, but they are commonly
caused by a genetic defect. If a symptomatic aetiology is suspected but cannot be
demonstrated, the condition is called cryptogenic epilepsy. (Shorvon et al. 2004)
23
Table 2 presents the 1989 ILAE classification of epilepsies and epileptic
syndromes, which is used in this thesis. According to new proposed terminology
and classification (ILAE 2005–2009) genetic, structural-metabolic and unknown
represent modified concepts to replace idiopathic, symptomatic and cryptogenic.
Effective classification of seizures and syndromes is indispensable for adequate
therapy and prognosis.
Table 2. International classification of epilepsies and epileptic syndromes
(Commission on Classification and Terminology, ILAE, 1989).
Class Classification
1. Localization-related (focal, local, partial)
1.1. Idiopathic (with age-related onset)
Benign childhood epilepsy with centrotemporal spikes
Childhood epilepsy with occipital paroxysm
Primary reading epilepsy
1.2. Symptomatic
Chronic progressive epilepsia partialis continua of childhood
1.3. Cryptogenic
The symptomatic and cryptogenic categories comprise syndromes of great individual
variability that are based on:
Seizure types (according to the International classification of Epileptic Seizures)
Anatomic localization: Temporal, frontal, parietal, and occipital lobe epilepsies
Bi- and multilobar epilepsies
Aetiology (in symptomatic epilepsies)
Specific modes of precipitation
2. Generalized
2.1. Idiopathic (with age-related onset, in order of age)
Benign neonatal familial convulsions
Benign neonatal convulsions
Benign myoclonic epilepsy of infancy
Childhood absence epilepsy (pyknolepsy)
Juvenile absence epilepsy
Juvenile myoclonus epilepsy (impulsive petit mal)
Epilepsy with grand mal (GTC) seizures on awaking
Other idiopathic generalized epilepsies not defined above
Epilepsies with seizure precipitated by specific modes of activation
2.2. Cryptogenic or symptomatic (in order of age)
West syndrome (infantile spasms, Blitz-Nick-Salaam-Krämpfe)
Lennox-Gastaut syndrome
Epilepsy with myoclonic-astatic seizures
Epilepsy with myoclonic absences
24
Class Classification
2.3. Symptomatic
2.3.1. Non-specific aetiology
Early myoclonic encephalopathy
Early infantile epileptic encephalopathy with suppression-burst
Other symptomatic generalized epilepsies not defined above
2.3.2 Specific syndromes (see the original reference)
3. Epilepsies and syndromes undetermined whether focal or generalized
3.1. With both generalized and focal seizures
Neonatal seizures
Severe myoclonic epilepsy of infancy
Epilepsy with continuous spike-waves during sleep
Acquired epileptic aphasia (Landau-Kleffner syndrome)
Other undetermined epilepsies not defined above
3.2. Without unequivocal generalized or focal features (e.g. many cases of sleep-grand
mal)
4. Special syndromes
4.1 Situation-related seizures (Gelegenheitsanfälle)
Febrile convulsions
Isolated seizures or isolated status epilepticus
Seizures due to acute metabolic or toxic factors such as alcohol, drugs, eclampsia
2.1.5 Diagnosis
Since a variety of conditions can cause episodes of transiently disturbed
consciousness or function, the identification of an epileptic seizure is important. If
a patient has had one unprovoked seizure with epileptiform discharges in
electroencephalography (EEG) or two or more unprovoked seizures, the diagnosis
of epilepsy can be made. The diagnosis of epilepsy is based on medical history
and a detailed description of events that occurred before, during and after a
suspected epileptic seizure. Eyewitness information is often essential. Detailed
clinical examination, focusing on neurological and cardiovascular evaluation is
needed, although the clinical examination does not often reveal abnormal findings
in cases of new onset epilepsy. Laboratory tests add little to the diagnostics of
epilepsy. EEG recording is usually conducted to detect possible spike or sharp
wave discharges. Electrocardiography (ECG) is obtained to exclude cardiac
abnormalities. Cerebral computerized tomography (CT) or magnetic resonance
imaging (MRI) scan is done to visualize possible focal structural pathology in the
brain. Because the examinations often yield normal results, the recognition and
25
diagnosis of an epileptic seizure is almost entirely based on medical history, and a
detailed description of the clinical features of the seizure is essential for the
diagnosis. (Shorvon et al. 2004, Elger & Schmidt 2008)
2.1.6 Prognosis
The prognosis of epilepsy varies greatly depending on the aetiology and type of
epileptic syndrome but the overall prognosis for patients with epilepsy is good in
terms of seizure remission. There are differences among published studies
regarding definitions of remission, duration of the follow-up, and the number of
seizures the patients have experienced, which makes comparison of different
studies difficult.
In epilepsy, three prognostic groups are generally considered: (1) spontaneous
remission (20–30% of the over all epilepsy patient population) as seen e.g. in
benign epilepsy with centrotemporal spikes or childhood absences; (2) seizure
remission achieved with AEDs (20–30%), which occurs in most focal epilepsies
and juvenile myoclonic epilepsy syndromes; (3) persistent clinical seizures
despite AEDs (30–40%), i.e. refractory epilepsy. (Kwan & Sander 2004)
Despite an overall good prognosis for seizure control, epilepsy is a potentially
life-threatening condition associated with increased mortality (Cockerell et al.
1997). In population-based studies, the mortality rates have been approximately
2–3 times higher in patients with epilepsy than in the general population
(Olafsson et al. 1998, Tomson 2000). Increased mortality rates are due to deaths
that are unrelated to epilepsy, e.g. ischaemic heart disease, neoplasm outside the
CNS, and to deaths in which epilepsy itself is the cause of death, e.g., suicides,
accidents and status epilepticus. (Hauser et al. 1980, Cockerell et al. 1994)
SUDEP is the most important epilepsy-related cause of death, reported to be
responsible for 18–25% of deaths among patients with medically-refractory
epilepsy (Walczak et al. 2001). SUDEP is discussed in detail in section 2.6.
2.2 Temporal lobe epilepsy
TLE is the most common epileptic syndrome. Anatomically TLE can be
subclassified according to seizure origin to those with seizures originating in the
mesial temporal structures (MTLE) and those with seizures beginning elsewhere
in the temporal lobe (e.g. neocortical temporal lobe epilepsy). (Shorvon et al.
2004)
26
In MTLE seizures originate from the limbic areas of the mesial temporal lobe
such as the hippocampus, amygdala and the parahippocampal gyrus, which are
the most epileptogenic regions of the brain. The main pathology associated with
MTLE is hippocampal sclerosis, which may result from previous status
epilepticus, complicated febrile convulsions, encephalitis or ischaemic insult
(Heinemann 2004). However, other mesial temporal pathologies, e.g. tumours and
congenital pathologies, may also result in MTLE. Recent studies have also
implicated a strong genetic role in the development of hippocampal sclerosis
(Kobayashi et al. 2001).
In MTLE, the seizures usually start in early childhood (Janszky et al. 2004),
but there may be a long seizure-free period from the primary insult before
unprovoked seizures develop. The seizures often respond well to AEDs at the
beginning, but when the seizures return in adolescence they become refractory to
medical treatment (Berg 2008). It has been widely accepted that there is a strong
correlation between hippocampal sclerosis and the severity of epilepsy. Patients
with TLE who have hippocampal sclerosis will become seizure-free in only 10–
30% of the cases with the use of adequate AED treatment, and these patients
should therefore be considered for surgical evaluation. (Shorvon et al. 2004)
2.3 Treatment of epilepsy
2.3.1 Antiepileptic drugs
The treatment of epilepsy is mainly based on drug treatment. In recent decades,
several new AEDs have been developed, and there are currently more than 20
AEDs available, making it challenging for physicians to master the optimal use of
these agents (Prunetti & Perucca 2011).
The goal of the treatment with AEDs is to achieve complete seizure freedom
with as few side effects as possible (Duncan et al. 2006). The treatment is usually
started after two or more unprovoked epileptic seizures, but may also be
considered in patients after a single seizure if specific prognostic factors indicate
a high risk of recurrence, e.g. in patients with an underlying brain disorder, when
the EEG shows interictal epileptiform abnormalities, or in patients who have a
high-risk epilepsy syndrome, such as juvenile myoclonic epilepsy. (Kälviäinen &
Keränen 2001, Shorvon et al. 2004, Brodie 2005) The decision on whether
treatment is appropriate requires careful consideration of the individual risk-
benefit ratio related to treatment, based on such issues as the type and frequency
27
of seizures, type of epilepsy syndrome, age and sex of the patient, presence of
associated medical conditions and the possible side effects of the AED chosen.
(Adult Epilepsy:Current Care summary,2008) In a few specific epilepsy
syndromes, such as benign Rolandic epilepsy, pharmacotherapy may be
unneccessary (Brodie 2005).
Monotherapy is preferred, and about 60–70% of patients with recent onset
epilepsy respond to AED treatment (Cockerell et al. 1995, Kwan & Brodie 2001).
Those who do not experience satisfactory seizure control with monotherapy often
require polytherapy (combination of two or more AEDs). If the seizures continue
at the maximally tolerated dose of the first appropriately chosen AED, and no
underlying aetiology for the seizures is identified, it may indicate refractoriness of
epilepsy (Kwan & Brodie 2000). Refractory epilepsy is usually defined as a
scenario where a patient continues to have seizures despite adequate use of two
well-tolerated AEDs (Kwan et al. 2010). There is currently no evidence to suggest
whether switching to monotherapy with another AED or adding another AED is
more effective in the treatment of seizures in subjects who fail their AED (Beghi
et al. 2003). It has been suggested that combining drugs with different
mechanisms of action is beneficial (Brodie 2005). However, the classification of a
patient’s epilepsy as drug resistant at a given point in time is valid only at the time
of assessment and does not necessarily imply that the patient will never become
seizure-free on further manipulation of AED therapy (Callaghan et al. 2007,
Schiller & Najjar 2008). Since many epilepsies are prone to undergo remission,
the possibility of discontinuation of the AED should be considered after 3–5
years’ seizure freedom (Schmidt & Gram 1996).
Combining AEDs requires an understanding of their pharmacology, in
particular their mechanism of action (Rogawski 2002, Elger & Schmidt 2008).
Although, the mechanisms of action of all AEDs are not fully understood, they
fall into a number of general categories. The molecular targets and clinical
efficacy of different AEDs are presented in Table 3.
28
Table 3. Molecular targets and the spectrum of clinical efficacy of antiepileptic drugs.
Drug Na+
Channels
Ca2+
Channels
GABAA
Receptor
GABA
Transaminase
GABA
Transporter
GABAB
Receptor
NMDA
Receptor
Clinical
Efficacy
Carbamazepine + Partial,
GTC
Oxcarbazepine + Partial,
GTC
Lacosamide + Partial
Phenytoin + Partial,
GTC
Lamotrigine + + Broad
Spectrumb
Zonisamidea + + Partial,
GTC,
myoclonicb
Ethosuximide + + Absence
Phenobarbital + + Broad
Spectrum
Benzodiazepines + Broad
spectrum
Vigabatrin + Partialb
Tiagabine + Partial
Gabapentin + + Partial
Felbamate + + + Broad
spectrum
Topiramatea + + Broad
spectrumb
Valproic acid + + + Broad
spectrum
Levetirecetam + Partial
GTC
aZonisamide and topimarate are weak carbonic anhydrase inhibitors bVigabatrin, lamotrigine, zonisamide and topimarate may be useful in treating infantile spasm
GTC Generalized tonic-clonic
Modified from Rogawski 2002 and Elger & Schmidt 2008
Sodium channel blockers
Carbamazepine (CBZ) is first-line or adjunctive therapy in partial seizures with
or without secondary generalization and is not recommended in the absence or
myoclonic seizures. CBZ is generally well tolerated but CNS side effects are
29
fairly common when the serum concentration is high. CBZ is highly (70–80%)
bound to serum proteins and metabolized almost entirely by the liver, and induces
the cytochrome P450 enzyme system in the liver, which may increase or decrease
the metabolism of other drugs, including other AEDs. (Brodie 1992, Sillanpää
2004) The main mechanism of action of CBZ is on neuronal sodium channel
conductance by reducing high-frequency repetitive firing of action potentials
(Sillanpää 2004), and CBZ slows the conduction velocity of both central and
peripheral nerves (Traccis et al. 1983, Mervaala et al. 1987). CBZ also blocks N-
acetyl-D-aspartate receptors (MacDonald 2002).
Due to the mechanism of action, CBZ is associated with a delay in
atrioventricular conduction and may induce brady-arrhythmias (Ladefoged &
Mogelvang 1982, Boesen et al. 1983, Kasarskis et al. 1992), although sinus
tachycardia has also been observed (Kasarskis et al. 1992). However, there is
evidence that CBZ, at therapeutic levels, has no or minimal effects on the heart
conduction system in the vast majority of patients (Kennebäck et al. 1992). In
addition, CBZ has been shown to increase the sympathetic tone in the autonomic
nervous system and to suppress both parasympathetic and sympathetic function
(Isojärvi et al. 1998). CBZ treatment has also been suggested to be associated
with an increased risk of SUDEP, but the findings are controversial (Kennebäck et
al. 1997, Timmings 1998, Nilsson et al. 1999, Walczak et al. 2001, Hesdorffer et
al. 2011).
Oxcarbazepine (OXC) is a 10-keto analogue of CBZ and its active
monohydroxy derivative limits the firing of sodium-dependent action potentials at
lower concentrations than CBZ. OXC has less potential for drug interactions than
CBZ since it is not metabolized by cytochrome P450-dependent enzymes. OXC is
used as monotherapy or adjunctive therapy in the treatment of partial seizures
with or without generalization. (Faught 2004) The most common side effects
early in the treatment that often lead to discontinuation of OXC affect the CNS.
These side effects include headache, dizziness, ataxia, and nausea (Faught 2004).
Furthermore, hyponatraemia is a common side effect of OXC therapy, especially
in the elderly and female patients (Pendlebury et al. 1989, Isojärvi et al. 2001).
Lamotrigine (LTG) acts by blocking voltage-dependent sodium and calcium
channels in the neural membranes of the brain, thus reducing the epileptic activity
(Meldrum 1996). LTG also inhibits gamma-amino butyric acid (GABA) release.
Furthermore, LTG has been shown to inhibit the cardiac rapid delayed rectifier
potassium ion current (Ikr) (Danielsson et al. 2005). Ikrblocking drugs are
30
generally considered to be associated with an increased risk of cardiac arrhythmia
and SUDEP (Danielsson et al. 2005).
LTG is indicated as an adjunctive or monotherapy in partial and generalized
epilepsies as well as in Lennox-Gastaut syndrome. Headache, skin rash and
nausea are common side-effects of LTG (Brodie et al. 1995). No ECG
abnormalities have been associated with the use of LTG (Betts et al. 1991).
Phenytoin (PHT) is used for partial and tonic-clonic seizures especially when
the seizures are generalized. PHT, as i.v. formulation, is also used to treat status
epilepticus. The mechanism of action is thought to be based on the drug’s
capacity to bind to and prolong the inactivation of voltage-dependent sodium
channels in neuronal cell membranes. (Eadie 2004) PHT has a large number of
interactions with AEDs and other drugs. PHT is highly protein bound (90%) and
metabolized by hepatic P450 enzymes, which may increase or decrease the
metabolism of other drugs, including other AEDs. PHT also has antiarrhythmic
properties and it depresses the hyperactivity of cardiac sympathetic nerves. (Eadie
2004) The main adverse cardiac effect of PHT reported is bradyarrhythmias,
although mostly in i.v. administration of the drug (Earnest et al. 1983). There is
also a case report of complete atrioventricular block with ventricular asystole in a
patient receiving i.v. PHT (Randazzo et al. 1995). The main advantage of PHT
over other AEDs is good efficacy with low cost, but long-term side effects make
it less attractive as a long-term treatment for the majority of epilepsy patients.
Gabaergic drugs
Tiagabine (TGB) is a GABA uptake inhibitor and it prolongs the duration of the
peak inhibitory postsynaptic current, consistent with temporarily sustained levels
of endogenously released GABA in the synapse. TGB has proven effective as
add-on therapy in patients with refractory partial seizures with or without
secondary generalization. (Schachter 1999)
Vigabatrin (VGB) is used as adjunctive therapy in partial and secondarily
generalized epilepsy (Dichter & Brodie 1996), and also for infantile spasms and
Lennox-Gastaut syndrome (Appleton et al. 1999). VGB inhibits presynaptic
GABA degradation by selective, enzyme-activated irreversible blockade of the
mitochondrial enzyme GABA transaminase. VGB is not widely used because its
use is associated with persistent peripheral visual field defects (Kälviäinen et al.
1999).
31
Drugs with other mechanisms of action
Ethosuximide is the first-line or adjunctive therapy in generalized absence
seizures. The mechanism of action against absence seizures is the reduction of
low threshold T-type calcium currents in thalamic neurons. (MacDonald & Kelly
1995)
Gabapentin (GBP) is used as monotherapy in adults with partial or
secondarily generalized epilepsy. It is well tolerated and does not have any
significant drug interactions, and is therefore, is easy to use. GBP is thought to
inhibit high voltage-activated calcium channels (α2δ subunit) (Elger & Schmidt
2008).
Valproate (VPA) differs structurally from other AEDs and its mechanism of
action has remained undefined until now. Different studies have suggested that
VPA increases GABA concentrations through the activation of the GABA-
synthesizing enzyme glutamic acid decarboxylase. However, VPA also blocks
voltage-dependent sodium channels and affects calcium (T) conductance. (Arroyo
2004) VPA is the drug of choice for primary generalized epilepsies and useful in a
wide spectrum of other epilepsies (Perucca 2002). VPA has been shown to be
associated with obesity and hyperinsulinaemia, which may promote
hyperandrogenism, anovulatory cycles, amenorrhoea and polycystic ovary
syndrome that are well-known side effects of VPA (Isojärvi et al. 1993, Rättyä et
al. 1999, Mikkonen et al. 2004).
Topiramate (TPM) is a broad spectrum AED with multiple mechanisms of
action. It enhances GABA action, but it also inhibits sodium conduction, AMPA
subtype glutamate receptors and L-type high-voltage-activated calcium channels.
TPM is used in partial and generalized epilepsies as adjunctive therapy and as
monotherapy if epilepsy is r efractory to commonly used AEDs. Its use is limited
by cognitive side effects. (Glauser 1999)
Levetiracetam (LEV) is well tolerated and effective against partial and
generalized seizures. The exact mechanism of action is not fully confirmed, but it
is thought that LEV binds to a synaptic vesicle protein, SV2A, and acts by
modulating its function (Elger & Schmidt 2008).
2.3.2 Surgery
Epilepsy surgery is defined as any neurosurgical intervention with the primary
goal of eliminating epileptic seizures. About 30 per cent of patients do not
32
achieve seizure freedom despite adequate AED treatment (Kwan & Brodie 2001,
Kwan & Sperling 2009), and for these patients epilepsy surgery may be a
therapeutic alternative. Recently, drug-refractory epilepsy has been defined as the
failure of adequate trials of two well-tolerated and appropriately chosen and used
AEDs to achieve sustained freedom from seizures (Kwan & Sperling 2009).
Trials can usually be completed during 2–3 years (Shorvon & Luciano 2007). If
epilepsy is not controlled within this time interval, it is unlikely to ever be
completely controlled with AEDs alone (Kwan & Brodie 2000) and the
evaluation of surgery is appropriate at that time.
Resective epilepsy surgery can be a curative therapy when the epileptic zone
can be identified, and results in complete seizure control in about 60–80% of this
type of patients with intractable epilepsy (Wiebe et al. 2001, Jutila et al. 2002,
Spencer & Huh 2008). Candidates for epilepsy surgery need to go through a
careful comprehensive pre-surgical evaluation.
2.3.3 Vagus nerve stimulation
General aspects
VNS is a non-pharmacological antiepileptic therapy for patients with refractory
seizures over the age of 12 years who are not candidates for resective surgery or
who have had resective surgery with unsatisfactory results (Uthman et al. 1993,
Ben-Menachem 2002). Absolute contraindications for implantation of VNS are
limited to previous left or bilateral cervical vagotomy. Since the approval of the
VNS Therapy System™ (Cyberonics Inc.) by the Food and Drug Administration
in the US in 1997, over 20,000 patients with epilepsy have been treated with VNS
therapy worldwide (Schachter 2006).
VNS is normally implanted below the left clavicle and the stimulating
electrodes are placed around the left vagus nerve distal to the branching of the
recurrent laryngeal nerve, thereby conveying the electrical signal produced by the
generator to the vagus nerve. The bipolar lead has two connector pins at one end,
which are plugged into the generator, and two separate helical silicone coils at the
other end. Each helix has three turns, with a platinum ribbon electrode within the
middle turn. (Schachter 2004) The implantation procedure is done with a
standardized methodology (Reid 1990).
The generator is individually programmed to stimulate the nerve
automatically. The output current, pulse width and frequency and the duration of
33
each stimulus can be adjusted according to the patient’s needs. In addition, extra
stimulation can be activated at pre-programmed settings through a magnet passed
over the generator in case of aura or a seizure. When the magnet is left in place
over the generator, the device is inactivated. This can be used to suspend
stimulation at times when side effects would be inconvenient. (Schachter 2004)
The anatomy of the vagus nerve is discussed detail in section 2.4.1.
Mechanism of action
Despite extensive experimental studies and some human data, the precise
mechanism of action of VNS is unknown. However, in recent years much
progress has been made through neurophysiological, neuroanatomical,
neurochemical and cerebral blood flow studies in understanding the underlying
mechanisms of action of VNS. In early animal studies VNS stimulation was
shown to induce increased EEG synchronization in non-epileptic animals,
depending on the frequency of stimulation (Zanchetti et al. 1952, Chase et al.
1967). The frequencies and output currents used in different studies vary
(Woodbury & Woodbury 1990, Zabara 1992). Naritoku et al. were the first to
identify some key structures in the neuronal network between the brainstem and
forebrain during VNS stimulation (Naritoku et al. 1995). They showed that VNS
alters multiregional neuronal activities of the brainstem and cortex, especially the
amygdala, a highly epileptogenic region that also plays a role in the
generalization of seizures. Another study showed that an increase in GABA or a
decrease of glutamate transmission in rats’ nucleus tractus solitarius (NTS)
reduces the severity of limbic seizures and provides a potential mechanism for the
seizure protection obtained with vagal stimulation (Walker et al. 1999).
Furthermore, in electrically kindled cats, a model for chronic epilepsy, it has been
shown that VNS delays the development of seizures induced by electrical
kindling in the amygdala suggesting a possible preventative effect of VNS on
epileptogenesis (Fernández-Guardiola et al. 1999).
In human studies, VNS has been observed to increase cerebral blood flow in
the brain, e.g. the thalamus, the right posterior temporal cortex, the left putamen
and the left inferior cerebellum (Ko et al. 1996, Henry et al. 1999). The increased
blood flow in the thalamus has been shown to have significant correlation with
long-term seizure control (Henry et al. 1999). Furthermore, VNS is thought to
affect A and B myelinated fibres, which may contribute to the antiseizure effect
(Handforth et al. 1998, Banzett et al. 1999, DeGiorgio et al. 2000).
34
Chronic VNS also appears to have an effect on various amino acids pools in
the brain. A cerebrospinal fluid study showed a significant increase in GABA
after 3 to 4 months of VNS (Ben-Menachem et al. 1995), which may be related to
the anti-seizure effect of VNS.
Efficacy, safety and tolerability of VNS
VNS treatment has been shown to be safe, well tolerated and effective in seizure
reduction (Amar et al. 1999, Morris, 3rd & Mueller 1999, DeGiorgio et al. 2000,
Schachter 2004, Wheeler et al. 2011) Furthermore, long-term follow-up studies
have shown improved seizure control over time (DeGiorgio et al. 2000, Ben-
Menachem 2002, Uthman et al. 2004). However, even after long-term treatment,
up to 25% of patients do not experience any positive effect of VNS (Ben-
Menachem 2002).
The most frequently encountered adverse effects are stimulation-related, such
as throat pain, coughing, and hoarseness, which are usually mild to moderate in
severity and resolve with reduction of the intensity of the current or
spontaneously over time (Handforth et al. 1998, Schachter & Saper 1998, Morris,
3rd & Mueller 1999, Boon et al. 2001). Lack of the typical CNS side effects seen
with most of the commonly used AEDs is one of the advantages of VNS
treatment compared to AEDs.
2.4 Autonomic nervous system
2.4.1 Anatomy of the autonomic nervous system
ANS is responsible for visceral functions by a complex reflectory system that
provides an effective mechanism in maintaining homeostasis and adapting to the
demands of changing external and internal conditions. Therefore, reactions of
ANS are linked to almost all the physiological and pathological conditions of the
human body, for example cardiovascular, gastrointestinal, urinary, sexual and
thermal functions. (Appenzeller 1990, Loewy 1990b)
The ANS is anatomically and functionally divided into three distinct
interacting divisions: the sympathetic, parasympathetic and entric nervous
systems (Appenzeller 1990, Iversen et al. 2000). The sympathetic and
parasympathetic nervous systems maintain balance in the tonic activities of many
visceral structures and organs (Appenzeller 1990), while the entric nervous
35
system in the wall of the gastrointestinal tract is responsible for the reflex activity
involved in peristalsis and segmentation during the passage of food through the
bowel (Jänig & McLachlan 1999). Both sympathetic and parasympathetic
nervous systems consist of a chain of two neurons, which are separated from each
other by the ganglio dividing the chain into pre- and postganglionic parts.
Acetylcholine is the neurotransmitter for both sympathetic and parasympathetic
preganglionic neurons as well as postganglionic parasympathetic neurons.
Sympathetic postganglionic neurons use noradrenaline as neurotransmitter, with
the exception of the neurons innervating sweat glands, which use acetylcholine.
(Loewy 1990b)
The sympathetic preganglionic neuron cell bodies lie in the spinal cord to
form the intermediolateral cell column of the thoracic and upper lumbar spinal
cord (T1-L4). The preganglionic neurons synapse with the paravertebral ganglio
located laterally to the spinal cord, called truncus sympathicus. The
parasympathetic preganglionic neurons arise either from nuclei in the brain steam
or from the intermediolateral cell column of the sacral spinal cord (S1-S3). They
leave the CNS via distinct cranial nerves, sacral ventral roots and pelvic
splanchnic nerves, projecting their axons directly to the organs they supply. The
postganglionic neurons are located in small ganglia just outside or even within the
wall of the target organ. (Loewy 1990b)
The vagus nerve is the main parasympathetic efferent nerve regulating
autonomic functions. It provides parasympathetic control of the heart, smooth
muscle (pharynx, oesophagus, larynx), glands of the viscera of the neck, the lungs
(bronchial constriction and pulmonary secretion), and the gastrointestinal system
(increased peristalsis and secretions). However, the vagus nerve is also a mixed
nerve composed of about 80% of afferent sensory fibres carrying information
arising from the head, neck and abdomen to the brain (Loewy 1990b). Somata of
the efferent fibres are located in the dorsal motor nucleus and nucleus ambicuus.
Afferent fibres have their origin in the nodose ganglion and primarily project to
the nucleus of the solitary tract (NTS). (Boon et al. 2001)
2.4.2 Cardiovascular regulation
Under normal conditions, the sinus node is the cardiac pacemaker. Although, the
heart possesses an inherited ability for spontaneous, rhythmic initiation of the
cardiac excitation impulse, sinus node activity is also regulated by the ANS
whereas the autonomic activity is regulated in the brainstem where the integration
36
of information from higher cortical centres and the periphery is analysed.
(Benarroch E.E. 1997)
The balance of parasympathetic and sympathetic influences is critical for
control of cardiac function, including HR, excitability and contractility. It is
known that sympathetic nerve fibres innervate the entire heart, including the sinus
node, atrioventricular conduction pathways and the arterial and ventricular
myocardium, while the vagus nerve innervates the sinus node, the atrioventricular
pathways and the atrial muscle (Kamath & Fallen 1993). Parasympathetic
activation decelerates the HR where as sympathetic activation increases it.
Furthermore, HR is mostly influenced by the right vagus nerve that has dense
projections primarily to the atria of the heart.
The central autonomic network controls autonomic functions in a tonic,
reflexive and adaptive manner and integrates autonomic with hormonal,
behavioural, immunomodulatory and pain-controlling responses to internal or
external environmental challenges. The central autonomic network is composed
of several interconnected areas distributed throughout the neuraxis including
central nucleus of amygdala, several nuclei of the hypothalamus and NTS.
(Benarroch E.E. 1997)
NTS is the major visceral sensory relay cell group in the brain and it receives
inputs from all the major organs of the body. Afferents from cardiac receptors,
pulmonary receptors, and gastrointestinal receptors project to specific areas in the
NTS. Furthermore, there are specific areas in the NTS to gain information from
the carotid sinus and aortic depressor nerves that transmit high-pressure
baroreceptor and chemoreceptor afferent information. The information is
processed in the NTS and used to affect a number of autonomic, neuroendocrine
and behavioural functions. (Loewy 1990a)
The NTS has widespread projections to numerous areas in the forebrain as
well as the brainstem including important areas for epileptogenesis such as the
amygdala and the thalamus. There are direct neural projections into the raphe
nucleus, which is the major source of serotonergic neurons and A5 nuclei that
contain noradrenergic neurons. Furthermore, there are numerous diffuse cortical
connections. (Rutecki 1990, McLachlan 1993)
Cardiovagal motoneurons are located in the nucleus ambiguus and dorsal
vagal nucleus (Kalia 1981) and both regions receive inputs from the NTS, the site
of termination of cardiovascular and respiratory afferents involved in
cardiorespiratory reflexes (Benarroch E.E. 1997). Nucleus ambicuus and dorsal
37
vagal nucleus also receive projections from the hypothalamus, amygdala and
insular cortex.
Hippocampal structures, especially the amygdala, are among the centres at
the highest level of cardiovascular autonomic control (Frysinger & Harper 1990).
Central nucleus of the amygdala receives inputs from the NTS and from the
parabrachial nucleus, and a number of other areas and fibres from the amygdala
project to the hypothalamic area, medial parabrachial nuclei, locus coeruleus and
raphe nuclei and to the NTS and to the dorsal motor nucleus of the vagus nerve,
being thus directly involved in the autonomic modulation of HR.
38
Fig. 1. Graph illustrating autonomic cardiovascular regulation. AV=atrioventricular
node, CAN=central autonomic network, DVN=dorsal vagal nucleus, NA=nucleus
ambiguous, NTS=nucleus tractus solitarius, SN=sinus node. (Modified after Loewy
1990a and Benarroch 1997).
NTS
Gastrointestinal Receptors
Cardiovascular Receptors
Respiratory Receptors
PulmonaryReceptors
NA, DVN=cardiovagal motoneuron
Parasympathetic nervous system Sympathetic neurvous system
HEARTSNAV
Central integration
Reflexes
VISCERAL AFFERENTS
CAN
Amygdala
Hypothalamus
HR ↓ HR ↑
39
2.5 Heart rate variability and its clinical implications
2.5.1 Physiological background of heart rate variability and heart rate dynamics
HR variability is a term that is used to describe the variations in beat-to-beat
fluctuations around the mean HR. It gives information about the sympathetic and
parasympathetic autonomic balance and other physiological control mechanisms
on cardiac function. A high variability in HR is a sign of good adaptability,
implying a healthy individual with well-functioning autonomic control
mechanism. (Task Force 1996)
Measurement of HR variability has become a widely used tool for assessing
cardiovascular autonomic function in various physiological and pathological
conditions (Lipsitz et al. 1990, Huikuri et al. 1993, Korpelainen et al. 1996,
Tulppo et al. 1996, Tomson et al. 1998, Ansakorpi et al. 2000, Pikkujämsä et al.
2001). Long-term, usually 24-hour ECG recording can be used to assess
autonomic nervous responses during normal daily activities in healthy subjects,
subjects with disease and in response to therapeutic interventions, e.g. exercise or
drugs. Furthermore, 24-hour ECG recordings are a non-invasive and easy
approach to gain information on cardiovascular function and HR variability, and
the measurements have good reproducibility if used under standardized conditions
(Kleiger et al. 1991).
The physiological mechanisms underlying the various measures of HR
variability differ from each other. The average HR and the standard deviation of
all normal-to-normal RR intervals over an entire recording (SDNN) as the time
domain measures of HR variability have been found to reflect well both
sympathetic and parasympathetic influences on HR variability (Bigger, Jr. et al.
1989, Kleiger et al. 1992). Careful editing to exclude ectopic beats, artifacts and
missed beats is required to calculate SDNN accurately (Kleiger et al. 2005).
Power spectrum analysis reflects the amplitude of HR fluctuations present at
different oscillation frequencies (frequency domain measures of HR variability).
Very low frequency (VLF) is found at frequency 0.005–0.04 Hz. The exact
physiologic mechanism of VLF is not understood in detail, but it is suggested that
VLF power reflects the thermoregulation or vasomotor activity (van
Ravenswaaij-Arts et al. 1993). Furthermore, VLF power is reduced by
angiotensin-converting enzyme inhibition, suggesting that it reflects the activity
of the renin-aldosterone system (Bonaduce et al. 1994).
40
The low frequency (LF) component is observed around 0.04–0.15 Hz and is
modulated by baroreflexes with a combination of sympathetic and
parasympathetic efferent nerve traffic to the sinus node (Kamath & Fallen 1993,
Taylor et al. 1998). An increase in LF power has been proposed as being a marker
of sympathetic activation (Kamath & Fallen 1993), although, the parasympathetic
regulation has been reported to have an influence on the LF power (Akselrod et
al. 1985, Pomeranz et al. 1985).
High frequency (HF) is found around 0.15–0.4 Hz and it reflects ventilatory
modulation of RR intervals (respiratory sinus arrhythmia) with the efferent
impulses on the cardiac vagus nerve, which is considered to be a marker of
parasympathetic activation (Pomeranz et al. 1985, Pagani et al. 1997).
Geometrical methods are techniques in which RR intervals are converted into
various geometrical forms. In Poincaré scatterograms, each RR interval is plotted
as a function of the previous one. The plots can be interpreted either visually or
quantitatively (Huikuri et al. 1996, Tulppo et al. 1996). The standard deviation of
the longitudinal axis of the plot (SD2) is a marker of long-term HR variability,
while the standard deviation of the vertical axis is a marker of short-term beat-to-
beat (SD1) (Huikuri et al. 1996). The former reflects partly physical activity of
the patients in addition to autonomic regulation of HR (Tulppo & Huikuri 2004).
The latter index is a more direct measure of cardiac vagal outflow. In fact, SD1 is
a more reliable index of cardiac vagal activity than the HF spectral component of
HR variability, when measured from ambulatory recordings (Tulppo et al. 1998).
One advantage of the Poincaré method over spectral analysis techniques is that it
is not sensitive to stationary irregularities and trends in RR intervals, therefore
being more suitable for HR variability analyses using ambulatory ECG recordings
(Tulppo et al. 1996).
Physical activity, emotional stimuli or reflexes of various kinds can cause
non-periodic changes to RR interval time series. Newer non-linear dynamic
methods based on chaos system theory have been developed to detect these
changes. These newer methods offer information on the quality properties of HR
fluctuation.
Detrended fractal scaling exponent, also referred to as short-term scaling
exponent α, is computed from detrended fluctuation analysis and is a measure of
the degree to which the RR interval pattern is random at one extreme, or
correlated at the other on a scale of 3–11 beats (Kleiger et al. 2005). Short-term
scaling exponent α, has been shown to predict sudden cardiac death in the random
population of elderly subjects (Mäkikallio et al. 2001). In addition, abnormal
41
short-term scaling measure α has been reported to be associated with life
threatening arrhythmias in patients with myocardial infarction (Mäkikallio et al.
1997, Mäkikallio et al. 1999b).
The slope β of the power-law relationship of HR variability reflects the
distribution of spectral characteristics of RR interval oscillations (Saul et al. 1988,
Bigger, Jr. et al. 1996). The physiological background for the spectral distribution
is not known exactly, but the observation of a significantly steeper slope in
denervated hearts (Bigger, Jr. et al. 1996) suggests that the autonomic nervous
system has an important role in determining long-term HR variability. Previous
studies have shown that altered long-term variability measurements predict
mortality in patients with impaired left ventricular function (Mäkikallio et al.
1999a), after stroke (Mäkikallio et al. 2004) as well as in the elderly (Huikuri et
al. 1998).
Approximate entropy (ApEn) is a measure that quantifies the predictability or
regularity of time series data. A low value indicates that the signal is deterministic
while a high value indicates randomness. In previous studies, physiological
ageing has been associated with a loss of ApEn (Lipsitz & Goldberger 1992,
Pikkujämsä et al. 1999) but the clinical significance has, yet, remained unclear.
2.5.2 Factors affecting heart rate variability
Previous studies with healthy people have shown that HR variability decreases
with normal ageing being lower in elderly people compared to middle-aged or
young subjects, and these age-related changes seem to be modified by gender
(Hayano et al. 1991, Pikkujämsä et al. 1999, Fukusaki et al. 2000, Jokinen et al.
2005). It has been shown that HR variability is lower in women than in men
(Ramaekers et al. 1998, Bonnemeier et al. 2003). The gender difference in HR
variability is most pronounced in subjects younger than 30 years, disappearing
with age by approximately 50 years of age (Umetani et al. 1998, Antelmi et al.
2004).
Many medications act directly or indirectly on the ANS and affect HR
variability measurements. Thus, the influence of medication needs to be taken
into account in the analysis of HR variation. Atropine has been observed to
abolish respiratory sinus arrhythmia (Akselrod et al. 1985). There are also studies
on the effects of drugs on HR variability performed with antiarrhythmic drugs,
anaesthetics, sedatives and chemotherapeutic agents (Task Force 1996). However,
42
further studies are needed to assess possible effect of different drugs on HR
variability.
Smoking and alcohol use reduces HR variability (Malpas et al. 1991, Lucini
et al. 1996) while exercise is shown to increase it (Tulppo et al. 1998, Hautala et
al. 2009).
2.5.3 Heart rate variability in pathological conditions
The analysis of HR variability has been used widely in quantifying risk in both
cardiac and non-cardiac diseases, e.g. stroke, diabetes mellitus, multiple sclerosis,
ischaemic heart disease, cardiomyopathy and congestive heart disease (Kleiger et
al. 1987, Ewing 1991, Bigger, Jr. et al. 1996, Mäkikallio et al. 2004). Reduced
HR variability has been associated with increased cardiac arrhythmogenic
mortality in patients with various heart diseases and overall mortality in the
elderly (Kleiger et al. 1987, Huikuri et al. 1994, Malliani et al. 1994, Mäkikallio
1996). Some studies have also associated low HR variability with sudden cardiac
death (Barron & Viskin 1998). Reduction of HR fluctuation has also been
reported in various neurological diseases, including stroke (Korpelainen et al.
1997), and Parkinson’s disease (Pursiainen et al. 2002), but its clinical
significance in these settings is still undefined.
Analysis based on non-linear dynamics of HR fluctuation seems to provide
prognostic information among patients with various cardiac diseases and reveal
alterations in HR dynamics not detectable with conventional analysis methods
(Peng et al. 1995, Mäkikallio et al. 1999a, Huikuri et al. 2000). One study
showed that altered short-term fractal scaling properties of HR indicate an
increased risk for cardiac mortality, particularly sudden cardiac death, in the
random population of elderly subjects (Mäkikallio et al. 2001). Furthermore, it
has been suggested that abnormal long-term HR dynamics predict post-stroke
mortality (Mäkikallio et al. 2004). Reduction in HR variability has been shown to
be associated with increased risk of mortality in septic patients as well (Garrard et
al. 1993, Buchman et al. 2002). However, there is currently no consensus about
the best available index of HR variability for clinical use or risk stratification.
43
2.6 Epilepsy and autonomic cardiovascular dysregulation
2.6.1 Ictal autonomic dysfunction
Epileptic seizures can provoke a variety of autonomic responses such as
cardiovascular, respiratory, gastrointestinal, cutaneous, urinary, and genital
manifestations, and also emotional and sexual feelings (Devinsky 2004). The
effects of seizure discharges on ANS are thought to be mediated through the
cortical, limbic and hypothalamic systems, and thus the seizures that arise from or
spread to areas in the central autonomic network can mimic stimulation of
autonomic afferents or modify autonomic expression (Goodman et al. 1990,
Oppenheimer et al. 1992).
Episodes of tachycardias are considered to be the most common ECG
changes during seizures (Smith et al. 1989, Leutmezer et al. 2003, Rugg-Gunn et
al. 2004). Bradycardias are thought to be a rare event (Devinsky 2004), and
asystole episodes even less frequent (Schuele et al. 2007). However, it has been
suggested that ictal bradycardias with or without asystoles are currently
underestimated (Nashef et al. 1996, Tinuper et al. 2001, Rugg-Gunn et al. 2004).
According to MRI and CT scan findings, it seems that these ECG findings
can occur in the absence of any obvious structural brain abnormality and the
presence of particular changes in HR does not express side of the seizure focus
(Reeves et al. 1996, Britton et al. 2006). It has also been shown that seizures
arising from the temporal lobe, especially mesial lobe onset seizures, are more
prone to elicit HR changes (Galimberti et al. 1996, Tinuper et al. 2001, Garcia et
al. 2001, Leutmezer et al. 2003, Britton et al. 2006). Given that in TLE seizures
arise near the centres controlling cardiovascular regulation, the high incidence of
HR changes during seizures with TLE does not surprise.
In one study, there was a higher risk of ictal ECG abnormalities when
seizures arose from sleep or from the left hemisphere when MRI showed
evidence of hippocampal sclerosis (Opherk et al. 2002). Furthermore, in patients
with epilepsy different abnormalities in ECG morphologies during or
immediately after seizures have been observed, e.g. ST-segment depression
(Opherk et al. 2002, Tigaran et al. 2003) or elevation (Nei et al. 2000, Nei et al.
2004), life-threatening asystoles (Liedholm & Gudjonsson 1992, Devinsky et al.
1997, Rugg-Gunn et al. 2000, Mascia et al. 2005) and total atrioventricular block
(Tigaran et al. 2002).
44
There have been attempts to identify seizures by analysing HR and HR
variability measurement in patients with epilepsy. Novak et al. found that there
are detectable HR variability changes minutes prior to the clinical onset of
complex partial seizures (Novak et al. 1999). Similarly, other studies have
confirmed these findings that seizures can be predicted using HR or HR
variability analysis (Kerem & Geva 2005, van Elmpt et al. 2006). However, these
methods are not currently in clinical use.
2.6.2 Interictal heart rate variation
During the interictal state the prevalence of cardiac arrhythmias has been noted to
be similar in patients with epilepsy to that in the healthy population (Blumhardt et
al. 1986, Massetani et al. 1997). However, increased interictal HR has been
observed in some patients with various types of epilepsy (Evrengul et al. 2005,
Harnod et al. 2008). These changes in HR have been suggested to occur due to
alteration of autonomic cardiac function.
Using conventional short- and long-term HR variability analysis methods, it
has been shown that patients with chronic epilepsy have dysfunction of both
parasympathetic and sympathetic nervous systems during the interictal state
(Frysinger et al. 1993, Massetani et al. 1997, Isojärvi et al. 1998, Tomson et al.
1998, Ansakorpi et al. 2000, Harnod et al. 2008), but the clinical significance of
these findings has not been established. Although the mechanisms leading to such
autonomic dysfunction are not yet clearly understood, it has been suggested that
diminished interictal HR variability in patients with TLE is due to the epileptic
process itself, rather than any specific AED regimen (Ansakorpi et al. 2002), but
the opposite has also been proposed (Devinsky et al. 1994, Tomson et al. 1998).
Overall, it is difficult to differentiate with certainty the influence of epilepsy itself
and that of AEDs.
Previous studies indicate that antiarrhythmic drugs with sodium channel
blocking properties are associated with increased mortality and reduced HR
variability. Regarding the AEDs, CBZ which is also a sodium channel blocker has
most often been suggested to be associated with the altered ANS function
observed in patients with epilepsy (Devinsky et al. 1994, Isojärvi et al. 1998,
Tomson et al. 1998, Ansakorpi et al. 2000, Persson et al. 2003). In addition, one
study in patients with medically intractable seizures reported increased cardiac
sympathetic activity during sleep induced by sudden discontinuation of CBZ
(Hennessy et al. 2001). However, in contrast to previous findings, one study
45
found that sympathetic autonomic dysfunction was less severe in patients using
CBZ or OXC compared with patients not using these drugs (Koseoglu et al.
2009).
In TLE seizures arise from the mesial temporal structures, i.e. the amygdala
and hippocampus, or neocortical regions, and damage in those areas may result in
abnormalities manifested by altered HR variation. This is supported by
observations that hippocampus sclerosis may be associated with decreased
interictal HR variation (Ansakorpi et al. 2004, Koseoglu et al. 2009). One
previous study found that epilepsy surgery does not affect HR variability (Persson
et al. 2006), although HR variability was reduced in epilepsy surgery candidates
before surgery (Persson et al. 2005). Indeed, these autonomic alterations seem not
to exist or are minor in early stages of epilepsy, and evolve with time along with
the epileptic process itself. However, the role of structural brain lesions and
chronic epilepsy in autonomic dysfunction is difficult to assess.
Impaired autonomic cardiac control in patients with epilepsy is of particular
interest considering that reduced HR variability has been shown to predict
mortality and sudden death in other conditions than epilepsy (Binder et al. 1992).
It is also interesting to note that chronic TLE is associated with reduced interictal
HR variability (Massetani et al. 1997, Tomson et al. 1998, Ansakorpi et al. 2002,
Mukherjee et al. 2009), since patients with chronic TLE seems to be at greater
risk for SUDEP. However, there are no prospective studies regarding progression
of changes in HR variation in patients with epilepsy in the long term.
2.6.3 Circadian heart rate variation
In animal studies clear diurnal patterns of seizures have been observed in various
epilepsy models (Quigg et al. 1998). It is also well known that night time seizures
are common in some frontal lobe epilepsies, e.g. autosomal dominant nocturnal
frontal lobe epilepsy, and myoclonic seizures in juvenile myoclonic epilepsy
occur predominantly after awakening in the morning. Furthermore, many
physiological functions, such as thermoregulation, wakefulness and sleep, show
diurnal variation (Hastings et al. 2007), and there is also a significant decrease in
arterial pressure in healthy subjects during sleep (Millar-Craig et al. 1978).
Previous studies have mostly analysed HR variability from full 24-hour ECG
recordings, although it is well established that HR and HR variability have a
circadian rhythm (Lombardi et al. 1992, Huikuri et al. 1994). Reduction of
circadian HR fluctuation has been reported in various cardiovascular and
46
neurological diseases, including stroke (Korpelainen et al. 1997), diabetes
mellitus (Bernardi et al. 1992), coronary artery disease (Huikuri et al. 1994),
hypertension (Chakko et al. 1993) and Parkinson’s disease (Pursiainen et al.
2002). After an acute myocardial infarct, suppressed circadian fluctuation seems
to be related to lethal arrhythmic events (Huikuri et al. 1992), and infants at risk
of sudden infant death syndrome show a significant reduction in HR variability
during sleep (Harper et al. 1978, Eiselt et al. 1993).
There is one study (Ferri et al. 2002) concerning changes in HR variation
during sleep in children with partial epilepsy. The results showed that during
sleep, patients with epilepsy tended to have an overall lower HR variability in
both time- and frequency-domain parameters, which was most evident for HF
absolute power. Therefore, LF/HF ratio was higher in patients than in normal
controls. In accordance with the previous finding, one study reported that patients
who later died of SUDEP were found to differ from other patients with refractory
epilepsy in that seizures during sleep induced a more pronounced increase in HR
than seizures during wakefulness (Nei et al. 2004). In this regard, one previous
study observed that diminution of circadian HR variation during the night may, in
fact, be a marker for an increased risk for SUDEP (Eppinger et al. 2004), and thus
the relationship between HR variability and altered HR variation during the day
and night time is interesting.
2.6.4 Effect of vagus nerve stimulation on cardiovascular autonomic function
Previous studies have only reported cardiac arrhythmias during lead tests upon
VNS device implantation (Tatum et al. 1999, Ali et al. 2004) However, some
studies have also reported cardiac rhythm changes during chronic VNS treatment
(Frei & Osorio 2001, Amark et al. 2007).
Despite the close interaction between the vagus nerve and the heart, there are
only few studies concerning the effects of VNS on HR variability. The most
important previous studies were performed only during wakefulness and after
short-term VNS treatment. One previous study found a significant increase in HF
component (Kamath et al. 1992) while other studies suggested that VNS does not
affect HR variation (Handforth et al. 1998, Setty et al. 1998). It has been
suggested that the cardiac rhythm does not change with the stimulation of the
vagal nerve during sleep (Murray et al. 2001).
47
Only one study has tried to explore the long-term effects of VNS on cardiac
vagal tone (Galli et al. 2003). In that study, long-term VNS therapy appeared to
have some effects on cardiac autonomic function, with a reduction of the HF
component during the night and a flattening of sympathovagal circadian changes.
During VNS treatment, the left vagus nerve is stimulated below the cardiac
branches of the vagus nerve, and this may explain why the cardiac function is
unaffected by routine VNS treatment. However, further studies are needed to
clarify the effect of VNS on cardiac autonomic control.
2.7 Sudden unexpected death in epilepsy
2.7.1 Definition
There has been a lack of consensus regarding the definition of SUDEP. The most
widely used definition has defined SUDEP as a “sudden, unexpected, witnessed
or unwitnessed, non-traumatic and non-drowning death in a patient with epilepsy,
with or without evidence of a seizure and excluding documented status
epilepticus, in which postmortem examination does not reveal a toxicologic or
anatomic cause of death”. (Nashef 1997) Due to differences in SUDEP
definitions and methodologies it is challenging to compare the results of different
studies on SUDEP.
2.7.2 Epidemiology
The risk of SUDEP is increased in the general epilepsy population, but the
reported incidence varies widely depending on criteria and definitions, study
methods, and in particular on the type of epilepsy population under study.
According to community-based studies, the incidence of SUDEP ranges from
0.09 to 2.3 per 1,000 person-years (Terrence, Jr. et al. 1975, Leestma et al. 1989,
Ficker et al. 1998, Langan et al. 1998, Lhatoo et al. 2001). Furthermore, SUDEP
incidence appears to increase steadily with increasing severity of epilepsy, from
1.5–5.9 per 1,000 person-years in refractory epilepsy cohort studies (Nashef et al.
1995, Timmings 1998, Walczak et al. 2001) up to 6.3–9.3 per 1,000 person-years
in epilepsy surgery group or patients who continued to have seizures after surgery
(Dasheiff 1991, Hennessy et al. 1999, Nilsson et al. 2003, Sperling et al. 2005).
However, it has to be remembered that also patients with newly diagnosed
epilepsy and patients whose epilepsy is in remission are at risk for SUDEP,
48
although the risk is lower (Harvey et al. 1993, Racoosin et al. 2001, Langan et al.
2005). In children the SUDEP rates are thought to be lower than in adult
population, varying between 0.11–0.43 per 1,000 person-years (Harvey et al.
1993, Camfield et al. 2002, Weber et al. 2005). However, according to a recent
study from a cohort of subjects with childhood-onset epilepsy the risk of SUDEP
was noted to be higher compared to previous studies (Sillanpää & Shinnar 2010)
There are currently only a few studies on the rates of SUDEP in patients with
VNS treatment. Annegers et al. found the rates of SUDEP and mortality in
patients with VNS treatment to be in accordance with patients with intractable
epilepsy of young adult age (Annegers et al. 1998). After two years’ follow-up
the rate of SUDEP seems to be lower in the same cohort of patients with VNS,
which may be due to improved seizure control (Annegers et al. 2000).
2.7.3 Aetiology
SUDEP was already recognized in the middle of the 19th century. During recent
decades the interest in SUDEP and SUDEP-related research has increased
significantly. The aetiology of SUDEP has remained elusive, but some risk factors
have been identified. Uncontrolled descriptive studies have identified a risk
profile: youth, male sex, chronic alcohol use, lack of compliance with treatment
(Leestma et al. 1989, Devinsky et al. 1994, Opeskin et al. 2000, Tellez-Zenteno et
al. 2005). Studies using living patients with epilepsy as controls have proposed
poor seizure control, polytherapy with AEDs, onset of epilepsy at young age and
long duration of epilepsy as risk factors for SUDEP (Nilsson et al. 1999, Walczak
et al. 2001, Langan et al. 2005, Hitiris et al. 2007). Furthermore, one recent study
showed that in childhood onset epilepsy the risk of SUDEP was especially high
among patients with active epilepsy (Sillanpää & Shinnar 2010). However,
otherwise healthy, compliant patients may also die unexpectedly (Earnest et al.
1992, Nashef et al. 1998).
Abnormalities in autonomic cardiovascular regulation and its connection with
SUDEP have been debated. It has been suggested that SUDEP may be caused by
dysfunction of the cardiovascular autonomic control, which exposes the patient to
cardiac arrhythmias, sinus arrest and neurogenic pulmonary oedema (Schraeder &
Lathers 1989, Nashef et al. 1996, Nashef et al. 1998, So et al. 2000). In
accordance with this, one study showed that complex partial, tonic, or generalized
tonic-clonic seizures caused central apnoea of significant duration in patients with
epilepsy who were on video EEG monitoring (Nashef et al. 1996). However,
49
apnoea might also represent only ictal symptom of temporal lobe seizures (Lee et
al. 1999). Recent studies have also proposed a genetic ion channel dysfunction
predisposing to the development of cardiac arrhythmia and possibly to SUDEP
(Tester et al. 2005, Tu et al. 2011).
Circumstances of death in SUDEP cases are noted to be remarkably uniform
in various studies. Patients are often found in bed or are known to have been
asleep before death (Leestma et al. 1989, Nashef et al. 1996, Kloster &
Engelskjøn 1999, Nei et al. 2004, Nobili et al. 2010) suggesting that the risk for
specific aetiologic mechanisms directly responsible for death may increase during
sleep and sleep-related seizures could differ pathophysiologically. Deaths
following a recent seizure are supported by evidence of acute neuronal injury in
the hippocampus in some patients with SUDEP (Thom et al. 2003).
The role of different AEDs in SUDEP has remained controversial. A large
case control study showed that compared to having been on one or two drugs,
absence of treatment with AEDs was a strong risk factor for SUDEP (Langan et
al. 2005). Similarly, some studies report non-compliance with AEDs to be a risk
factor (George & Davis 1998, Williams et al. 2006). There are few studies that
have tried to analyse whether there is an association between specific AEDs and
increased incidence of SUDEP. One study found an association between SUDEP
and CBZ treatment (Timmings 1998). In contrast, another study found no
difference between monotherapy with CBZ or phenytoin in relation to SUDEP
incidence (Nilsson et al. 1999). Moreover, LTG has also been suggested to be
associated with increased risk for SUDEP (Aurlien et al. 2007, Hesdorffer et al.
2011). Overall, the association between different AEDs and SUDEP is
challenging. AEDs might prevent SUDEP by improving seizure control.
Conversely, abrupt AED withdrawal can considerably increase sympathetic tone
during sleep and the occurrence of adverse cardiac arrhythmias (Kennebäck et al.
1997, Hennessy et al. 2001). However, there is currently no well-defined reason
to avoid any particular AED to reduce the risk of SUDEP (Walczak 2003).
The results from previous studies concerning epilepsy surgery and its
possible association with SUDEP are conflicting. Some studies indicate that
patients who remained seizure-free after resective temporal lobe surgery had
lower SUDEP rates compared to those who continued to have seizures
postoperatively (Sperling et al. 1999, Salanova et al. 2002). Findings regarding
whether there are differences in SUDEP rates between patients with drug resistant
epilepsy after surgery compared to those who were medically treated vary a lot
(Vickrey et al. 1995, Nilsson et al. 2003, Stavem & Guldvog 2005).
50
Studies concerning seizure-related changes in cerebrovascular autoregulation
and SUDEP are limited. It has been suggested that an electrical cerebral shut-
down might play a role in the mechanism of SUDEP. This hypothesis is based on
observation in patients with SUDEP cases where EEG activity suddenly started
flattening before the occurrence of any fatal cardiac or respiratory arrest was
shown (McLean & Wimalaratna 2007). It has been postulated that a primary
cause could be an alteration of cerebral blood flow autoregulation leading to a
sudden drop of cerebral perfusion and subsequent cessation of electrical activity
(Surges et al. 2009b).
In conclusion, SUDEP seems most often to be associated with chronic
uncontrolled epilepsy with a wide range of different risk factors mentioned above
(Tomson et al. 2008). Therefore, identification of individual patients at risk of
SUDEP is challenging.
51
3 Aims of the study
The aim of the study was to evaluate autonomic cardiovascular regulation in
patients with TLE and in patients with refractory epilepsy during VNS treatment,
using 24-hour ambulatory ECG recordings. The specific aims of the individual
studies were:
1. To assess HR variation in patients with well-controlled or refractory TLE (II).
2. To evaluate whether HR variation changes with time in patients with chronic
TLE (II).
3. To assess circadian HR dynamics in patients with well-controlled or
refractory TLE (I).
4. To evaluate whether circadian HR dynamics change with time in patients
with chronic TLE (III).
5. To study the effects of VNS therapy on interictal HR variability in patients
with refractory epilepsy (IV).
53
4 Subjects and methods
This study was carried out at the Department of Neurology at Oulu University
Hospital, Finland, during the years 1999–2011. The study was approved by the
Ethics Committee of the Northern Ostrobothnia Hospital District, and carried out
according to the principles of the Declaration of Helsinki. All patients and control
subjects gave their informed consent before their inclusion in the study.
4.1 Subjects
This study consisted of three different study populations. The clinical
characteristics of the patients and the control subjects in the individual studies (I-
IV) are presented in Table 4. In Studies I-III consecutive patients with refractory
TLE and well-defined lateralization of the epileptic focus seen at the Department
of Neurology, University Hospital of Oulu, were considered for the study. A
similar number of patients with well-controlled TLE and well-defined
lateralization of the epileptic focus were identified as control subjects.
Thirty-seven patients with TLE participated in Study I. Of these, 17 patients
had refractory TLE and 20 had well-controlled TLE. An interictal EEG recording
was obtained from all the patients. Normal EEG or general slowing was seen in
15 patients. Left temporal focal slow waves, or irritation, or both were detected in
15, whereas right temporal focal abnormalities were seen in 7 patients
Thirty-six patients with TLE participated in Studies II-III. Of these, 18
patients had refractory TLE and 18 had well-controlled TLE.
In Studies I-III patients with manifestations of other disease (e.g. diabetes
mellitus, alcoholism or cardiopulmonary disease), or other central or peripheral
nervous system disorders or medication (besides AEDs) known to affect the ANS
were excluded from the study. Female patients who were pregnant or lactating
were also excluded.
The exclusion criteria were met in male patients more often than female
patients, which resulted in a smaller number of male patients in the study. The
patients were considered to have refractory TLE if their seizures were not
controlled despite appropriate use of AEDs and they had not been considered
suitable candidates for resective epilepsy surgery. Patients were considered not to
be appropriate candidates for resective epilepsy surgery if the MRI was normal, if
the epileptic focus could not be identified, if the patient refused surgery, or if the
patient had psychiatric problems or other contraindications for surgery. The
54
patients were considered to have well-controlled TLE if they were seizure-free or
were experiencing less than two seizure per year.
Fourteen patients with refractory epilepsy seen in the outpatient clinic of the
Department of Neurology, Oulu University Hospital were included in Study IV.
None of these subjects were considered candidates for resective surgery after a
careful presurgical evaluation. A Neurocybernetic Prosthesis (NCP Generator
Cyberonic, Webster, TX, USA) was implanted in all the patients for the treatment
of refractory epilepsy.
The control group consisted of 101 healthy age- and sex-matched subjects
chosen from a group of healthy individuals participating in a study comparing the
characteristics of hypertensive and normotensive subjects who in turn had been
randomly selected by their personal social security numbers from the general
population of Oulu. None of them had medication or diseases affecting the ANS
in their medical history (Studies I, III-IV).
Ta
ble
4. C
ha
rac
teri
sti
cs o
f s
tud
y p
op
ula
tio
ns
.
S
tud
y I
Stu
die
s II
-III
Stu
dy
IV
Refr
act
ory
W
ell-
contr
olle
dC
ontr
ol
Refr
act
ory
W
ell-
contr
olle
d
Contr
ols
Patie
nts
C
ontr
ol
Base
line
Follo
w-u
pB
ase
line
Follo
w-u
pB
ase
line
(n=
17)
(n=
20)
(n=
37)
(n=
18)
(n=
18)
(n=
18
) (n
=1
8)
(n=
36
) (n
=1
4)
(n=
28
)
Age, ye
ars
32.1
±7.2
32.2
±6.6
32.2
±8.3
32.4
±7.1
32.2
±6.6
32.7
±8.5
34.3
±9.3
34.4
±9.3
Male
/fem
ale
4/1
3
9/1
1
13/2
4
4/1
4
9/9
13/2
3
8/6
16/1
2
Dura
tion o
f epile
psy
22.7
±10.0
14.2
±10.1
N
.A.
22.4
±9.8
13.9
±10.4
N
.A.
27.3
±10.8
N.A
.
Se
izu
re-f
ree
(n
o.
of
pa
tien
ts)
0
11
N
.A.
0
1
11
1
6
N.A
. 0
N
.A.
Seiz
ure
s per
month
20.3
±38.2
0.0
3±0.0
7
N.A
. 20.9
±37.2
14.9
±26.8
0.0
2±0.0
5
0.8
±3.5
N
.A.
48.4
±38.3
N.A
.
Antie
pile
ptic
medic
atio
n
Mo
no
the
rap
y:
Ca
rba
ma
zep
ine
(C
BZ
) -
10
-
- 1
1
0
4
- -
-
Oxc
arb
aze
pin
e (
OX
C)
4
7
- 4
-
7
7
- -
-
Ph
en
yto
in (
PH
T)
- 1
-
- -
- -
- -
-
La
mo
trig
ine
(L
TG
) -
1
- -
- 1
2
-
- -
Po
lyth
era
py:
-
CB
Z w
ith o
the
r A
ED
(s)
7
1
- 7
4
-
1
- 6
-
OX
C w
ith o
the
r A
ED
(s)
6
- -
7
9
- -
- 4
-
LT
G w
ith o
the
r A
ED
(s)
- -
- -
4
- -
- 3
-
Oth
er
AE
D c
om
bin
atio
n
- -
- -
- -
- -
1
-
No
me
dic
atio
n
- -
- -
- -
4
- -
-
Abbre
viatio
ns:
The v
alu
es
are
means
± s
tandard
devi
atio
n. N
.A. =
not applic
able
.
55
56
4.2 Methods
4.2.1 Clinical examination (Studies I-IV)
All the patients were carefully interviewed and clinically examined. Their
epilepsy and seizure type was classified according to the recommendations of the
International League Against Epilepsy (ILAE 1981,1989). An interictal EEG
recording was obtained from all the patients.
In the case histories obtained with a structured interview, special attention
was given to the following subjective symptoms reflecting possible autonomic
dysfunction: cardiac arrhythmias, dizziness due to orthostatic hypotension,
changes in sweating, and sexual malfunction. Laboratory screening (liver and
renal functions, serum electrolytes and basic haematological indices) was in
general normal in all the study patients. Blood samples for the laboratory tests
were taken in the morning after the subjects had taken the morning dose of their
medication, but before the start of the 24-hour ECG recording. MRI or CT of the
brain was performed on all the patients.
A presurgical evaluation (MRI, 24-hour EEG –video telemetry recording)
was made for each subject before the decision about the implantation of VNS was
made, and none of the patients were considered eligible candidates for resective
epileptic surgery after these evaluations (Study IV).
4.2.2 Adjustment and use of vagus nerve stimulator (Study IV)
VNS (the model 100 Neurocybernetic prosthesis; Cyberonics, Pulse Generator)
was implanted using a previously described method (Reid 1990) between
February 1999 and January 2002. The starting level of stimulation was 0.25mA,
stimulation frequency 30Hz, pulse width 500ms, on time 30sec and off time
5min. The output current was generally increased by 0.25mA every two weeks.
The output current was increased to a clinical response. If the patient experienced
intolerable side effects, the output current was decreased or the increase was
delayed for another two weeks. The mean (range) stimulation output intensity one
year after the VNS implantation was 2.9 (1.75–3.5) mA, stimulation frequency
30Hz, pulse width 500ms, on time 30sec and mean (range) off time 4.7 (3.0–5.0)
min.
57
4.2.3 Analysis of heart rate behaviour (Studies I-IV)
ECG recordings
All the subjects in all studies (I-IV) were monitored for 24 hours with an
ambulatory two-channel ECG recorder (CardioCorder® model 456A, Del Mar
Medical, Irvine, California, USA). In study I, the ECG recording was done once.
In studies II-III, the ECG recording was done at baseline and after a mean follow-
up of 6.1 years, and in study IV the ECG recording was done in all the patients
before and one year after the VNS implantation. In the control subjects the 24-
hour ECG recording was performed once. The patients with epilepsy and the
control subjects were allowed to perform their daily activities during the
recording. The patients with epilepsy were also asked to keep a diary to document
any seizures or all the activities during the recording.
The ECG data from the recordings were sampled digitally and transferred
from an Oxford Medilog scanner (Oxford Instruments, UK) to a microcomputer
for analysis of HR variability. All the RR interval time series were first edited
automatically, after which careful manual editing was performed by visual
inspection of the RR intervals. Each RR interval time series was passed through a
filter that eliminates premature beats and artefacts and deletes the filling gaps
(Huikuri et al. 1993, Makikallio et al. 1996). In the final analysis of linear
components of HR variability, 24-hour measurements were divided into segments
of 3,600 RR intervals, and in the analysis of non-linear components of HR
variability, 24-hour measurements were divided into segments of 3,600 and only
segments with more than 85% sinus beats were included. The mean values of the
night hours (from midnight to 6 AM) and the day hours (from 9 AM to 9 PM)
were calculated (Studies I, III, IV).
Time domain and spectral analysis
SDNN from the entire recording was used as a time domain measure of HR
variability. In the frequency domain analysis of HR variability, a linear detrend
was applied to the RR interval data segments of 512 samples to make them more
stationary. The size of 20 was used as the model order in the analysis of the RR
interval data. The power spectra were quantified by measuring the area in three
frequency bands: 0.005 to 0.04 Hz, VLF, 0.04 to 0.15 Hz, LF and 0.15 to 0.4 Hz,
HF. The VLF power spectra were analysed and calculated from the entire
58
recording period, while the LF and HF power spectra were analysed from the
time window of 512 RR intervals, as recommended by the Task Force of the
European Society of Cardiology and the North American society of Pacing and
Electrophysiology (Task Force 1996).
Poincaré plot analysis
For quantitative two-dimensional vector analysis, the standard deviation of
instantaneous beat-to-beat RR interval variability (SD1) and continuous long-
term RR interval variability (SD2) were analysed, and visually presented as
Poincaré plot scattergrams, in which each RR interval is plotted as a function of
the previous one (Tulppo et al. 1996). In the computerized analysis, the Poincaré
plot was first turned 45° clockwise, and the standard deviation of the plot data
was then computed around the horizontal axis, passing through the data centre
(SD1). The standard deviation of the continuous long-term RR intervals was
quantified by turning the plot 45° counterclockwise (SD2) and by computing the
data points around the horizontal axis, passing through the centre of the data.
(Myllylä et al. 2002)
Approximate entropy analysis
A value of ApEn is a measure that quantifies the regularity of time series data. It
measures the logarithmic likelihood that runs of patterns (beat-to-beat difference
of RR interval length) are close in the next incremental comparisons. A greater
likelihood of remaining close (high regularity) produces smaller ApEn values, and
conversely, random data produce higher values. Two input variables, m and r,
must be fixed to compute ApEn, and m=2 and r=20% of the standard deviation of
the data sets were chosen as suitable values on the basis of previous findings of
good statistical validity. (Pincus & Viscarello 1992, Pincus & Goldberger 1994,
Myllylä et al. 2000)
Fractal correlation analysis
To quantify fractal correlation properties of HR, the detrended fluctuation
analysis technique, which is modified root-mean-square analysis of random walk,
was used. The HR correlation properties were defined for the short-term (below
59
11 beats, α) correlation of RR interval data (short-term scaling exponent).
(Mäkikallio et al. 1997, Myllylä et al. 2002)
Power-law relationship analysis
The power-law relationship of RR interval variability, a spectral measure
reflecting the distribution of the spectral characteristics of the RR interval
oscillations, was calculated from the frequency range of 10-4 to 10-2. The point
power spectrum was logarithmically smoothed in frequency domain, and the
power was integrated into bins spaced 0.0167 log(Hz) apart. A robust line fitting
algorithm of log(power) on log(frequency) was then applied to the power
spectrum between 10-4 and 10-2, and the slope of this line was calculated. This
frequency band was chosen on the basis of previous observations regarding the
linear relationship between log(power) and log(frequency) in this frequency band.
(Saul et al. 1988, Bigger, Jr. et al. 1996, Myllylä et al. 2002)
4.2.4 Statistical analysis
All data were analysed using the SPSS for Windows SPSS versions 10.0 (I), 11.5
(IV) and 14.0 (II-III) (SPSS Inc. Chicago, Illinois, USA). The results are mostly
given as medians (interquartile range). The changes of continuous variables
during the follow-up were analysed by the Mann-Whitney two-sample test. Two-
sided p-values were used. The Mann-Whitney two-sample test was used due to a
small number of cases and because of a normal distribution pattern could not be
detected. When comparisons were made between the groups, the Wilcoxon
signed-rank test was used. Spearman’s correlation coefficients were used to
estimate the correlation of the HR variables with the duration of epilepsy and the
age of the patients. The Mann-Whitney two-sample test was also used to analyse
the association of HR variability with the laterality of the seizure focus and the
carbamazepine (CBZ) and oxcarbazepine (OXC) monotherapy. The Mann-
Whitney two-sample test was also used to analyse the association of HR
variability with the laterality of the seizure focus and the carbamazepine (CBZ)
and oxcarbazepine (OXC) monotherapy (Studies I-III). The limit of statistical
significance was set at p<0.05 in all studies.
61
5 Results
5.1 Clinical evaluation of autonomic nervous system function
The patients did not complain of any particular symptoms referring to ANS
dysfunction. In the clinical examination, no signs of autonomic dysfunction were
found. The clinical cardiorespiratory findings, including baseline blood pressure
and neurological examination, were normal in all patients. None of the patients
had significant cardiac arrhythmias in the ECG recordings. The results of the
basic laboratory screening tests were within normal range and serum drug levels
within therapeutic ranges in all the patients.
In studies II-III, during the follow-up, one patient with refractory TLE
underwent resective surgery (mesial temporal lobectomy) and became seizure
free. One patient with well-controlled TLE at baseline experienced an increase in
seizure frequency during the last year of the follow-up. Four patients with well-
controlled TLE had remained seizure-free and were on no medication at the
follow-up.
5.2 Cardiac regulation in temporal lobe epilepsy
5.2.1 Long-term heart rate dynamics (Study II)
In study II, all mean values of HR variability measures tended to be lower in
patients with refractory TLE compared to those with well-controlled TLE at
baseline and after the follow-up, although statistically significant differences
could not be found (p>0.05) (Table 5). After the follow-up, the Poincaré
components SD1 (p=0.039) and SD2 (p=0.001) were further decreased in patients
with refractory TLE compared to the baseline values. There were no statistically
significant differences in any HR variability measures (p>0.05) in patients with
well-controlled TLE when the follow-up values were compared to the baseline.
Figure 2 presents representative examples of the power spectrum analysis of HR
variability.
62
Table 5. Measures of HR and HR variability in patients with refractory and well-
controlled temporal lobe epilepsy at baseline and after follow-up of mean duration of 6
years (Study II).
Variables Refractory Well-controlled
Baseline At 6 years Baseline At 6 years
(n=18) (n=18) (n=18) (n=18)
RRI (ms) 823 (775–920) 782 (734–830)* 823 (789–897) 778 (720–861)
SDNN(ms) 143 (126–170) 129 (105–147) 155 (107–189) 175 (121–198)
LF (ms x ms) 776 (523–979) 612 (392–921) 998 (664–1377) 893 (501–1710)
HF (ms x ms) 374 (265–769) 263 (210–564) 550 (383–931) 510 (222–865)
VLF (ms x ms) 1321 (1017–1781) 1119 (853–1738) 1630 (1096–2520) 1706 (1049–2849)
SD1 24 (19–34) 22 (17–27)* 28 (20–37) 28 (20–34)
SD2 118 (101–138) 96 (86–107)** 125 (99–161) 115 (96–147)
α 1.20 (1.09–1.36) 1.21 (1.11–1.30) 1.23 (1.11–1.33) 1.21 (1.16–1.27)
Slope β −1.3 (−1.5…−1.2) −1.29 (−1.34…−1.17) −1.3 (−1.5…−1.2) −1.28(−1.35…−1.16)
RRI, R-R interval; SDNN, SD of all RR intervals; LF, low-frequency; HF, high-frequency; VLF, very low-
frequency; SD1, beat-to-beat variability measure from Poincaré; SD2, long-term variability measure from
Poincaré; α, short-term fractal correlation parameter; Slope β, long-term power-law slope. Values are
presented as medians (interquartile range).
*p<0.05, **p<0.01 compared both groups separately before and after follow-up, the Wilcoxon signed-rank
test.
Fig. 2. Example of two-dimensional vector analysis (Poincaré plots) in a healthy
subject (A), in a patient with a well-controlled TLE (B) and in a patient with refractory
TLE (C)
5.2.2 Circadian heart rate variation (Study I)
The HR variability measurements showed that patients with TLE have
dysfunction of autonomic cardiac regulation which is more pronounced during
the night time. The SDNN (p=0.001), the spectral components LF (p=0.001) and
CRefractory patient
A
Healthy subject
B
Well-controlledpatient
63
HF (p=0.004), the SD1 (p=0.001) and SD2 (p=0.001) Poincaré components and
the short-term fractal property component α (p=0.01) of the TLE patients were
significantly lower than those of the control subjects during the night and also
during the day (p<0.05). The suppression of the circadian fluctuation of the HF
and LF spectral components in TLE patients compared to the healthy control
subjects is presented in Figure 3. There were no differences in any HR variability
measurements (p>0.05), apart from ApEn in the night (p=0.026), between the
refractory and well-controlled epilepsy patients when compared to each other.
The mean night-to-day ratios (median, interquartile range) of the SDNN, SD1 and
SD2 Poincaré components were lower in the epilepsy patients (SDNN 0.80, 0.68–
1.07, p=0.014; SD1 1.16, 0.98–1.44, p=0.030; SD2 0.78, 0.62–0.92, p=0.007) than
in the control subjects (SDNN 1.07, 0.90–1.17, SD1 1.48, 1.07–1.70, SD2 0.98,
0.74–0.97). There were no differences in the mean night-to-day ratios in the
spectral components LF and HF, ApEn and the short-term fractal property
component α (p>0.05) between patients with TLE and the control subjects.
Furthermore, none of the night-to-day ratios of the HR variability measures were
different between the refractory and the well-controlled epilepsy patients
(p>0.05).
64
Fig. 3. The 24-hour circadian fluctuation of low-frequency (LF) and high-frequency (HF)
components of HR variability (medians) in patients with TLE (circles) and in healthy
controls (squares). *p<0.05, **p<0.01, ***p<0.001 for comparison between patients and
controls, Mann-Whitney two-sample test.
***
***
***
***
***
*** *
**
*
**
**
***
*
**
**
**
**
*
*
**
**
**
**
** *
**
0
500
1000
1500
2000
2500
3000
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7
Time (hour of the day)
LF
po
wer
(ms
xm
s) ***
***
**
**
**
0
200
400
600
800
1000
1200
1400
1600
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7
Time (hour of the day)
HF
po
wer
(ms
xm
s) *
65
5.2.3 Long-term changes in circadian heart rate variation (Study III)
In study III the circadian HR variability was reduced in patients with TLE at
baseline when compared to healthy control subjects. After 6 years’ follow-up, the
spectral components LF (p=0.039) and HF (p=0.001), and the Poincaré
component SD1 (p=0.002) were further decreased in patients with refractory TLE
compared to the baseline values during the night. During the day, the spectral
components LF (p=0.004) and HF (p=0.006) and the Poincaré components SD1
(p=0.006) and SD2 (p=0.001) were further decreased in patients with refractory
TLE compared to the baseline values (Tables 6 and 7). There were no statistically
significant differences in any HR variability measures (p>0.05) in patients with
well-controlled TLE when the baseline values were compared to those at the
follow-up.
Ta
ble
6. T
he n
igh
t ti
me
(0
0.0
0 A
M-0
6.0
0 A
M)
va
lue
s f
or
the
tim
e d
om
ain
, fr
eq
ue
ncy
do
ma
in a
nd
fra
cta
l m
eas
ure
s o
f H
R v
ari
ab
ilit
y
in p
ati
en
ts w
ith
re
fra
cto
ry a
nd
we
ll-c
on
tro
lled
ep
ile
psy
an
d in
co
ntr
ol
su
bje
cts
(S
tud
y I
II).
Variable
s P
atie
nts
with
epile
psy
C
ontr
ols
Refr
act
ory
W
ell-
contr
olle
d
Base
line
At 6 y
ears
B
ase
line
At 6 y
ears
B
ase
line
(n=
18)
(n=
18)
(n=
18)
(n=
18)
(n=
36)
LF
(m
s x
ms)
613 (
452–1170)+
+
546 (
347–1017)*
884 (
584–1244)+
+
835 (
542–2022)
2386 (
1010–3252)
HF
(m
s x
ms)
493 (
310–1158)+
317 (
199–588)**
631 (
289–838)+
717 (
338–1054)
1156 (
412–2938)
SD
1
27 (
21–39)+
+
22 (
18–32)*
31 (
20–34)+
+
27 (
18–41)
39 (
31–69)
SD
2
89 (
80–121)+
+
81 (
68–102)
105 (
82–117)+
+
115 (
73–151)
147 (
106–185)
α
1.0
7 (
0.8
6–1.2
4)
1.1
4 (
0.9
8–1.2
6)
0.9
6 (
0.7
8–1.1
8)
1.0
8 (
0.9
7–1.2
1)
1.0
3 (
0.8
2–1.2
1)
Ap
En
1.3
9 (
1.3
0–
1.4
9)
1.3
0 (
1.2
0–
1.4
6)
1.2
6 (
1.1
1–
1.4
5)
1.2
7 (
1.1
2–
1.3
6)
1.2
8 (
1.1
1–
1.4
6)
LF
, lo
w-f
req
ue
ncy
; H
F,
hig
h-f
req
ue
ncy
; S
D1,
be
at-
to-b
ea
t va
ria
bili
ty m
ea
sure
fro
m P
oin
caré
; S
D2
, lo
ng-t
erm
vari
abili
ty m
easu
re f
rom
Poin
caré
; α
, sh
ort
-te
rm
fra
cta
l co
rre
latio
n p
ara
me
ter;
Ap
En
, a
pp
roxi
ma
te e
ntr
op
y. V
alu
es
are
pre
sente
d a
s m
edia
ns
(inte
rquart
ile r
ange).
* p
<0.0
5,
** p
<0.0
1 f
or
com
pariso
n p
atie
nts
with
refr
act
ory
and w
ell-
contr
olle
d T
LE
separa
tely
befo
re a
nd a
fter
follo
w-u
p, th
e W
ilcoxo
n s
ign
ed
-ra
nk
test
. +p
<0
.05
, +
+p
<0.0
1 f
or
com
pariso
n b
etw
een p
atie
nts
with
refr
act
ory
and w
ell-
contr
olle
d T
LE
separa
tely
and c
ontr
ols
, M
ann-W
hitn
ey
two-s
am
ple
test
.
66
Ta
ble
7. T
he d
ay
-tim
e (
09
.00 A
M –
09
.00
PM
) va
lue
s f
or
the t
ime d
om
ain
, fr
eq
uen
cy d
om
ain
an
d f
racta
l m
easu
res o
f H
R v
ari
ab
ilit
y
in p
ati
en
ts w
ith
re
fra
cto
ry a
nd
we
ll-c
on
tro
lled
ep
ile
psy
an
d in
co
ntr
ol
su
bje
cts
(S
tud
y I
II).
Variable
s P
atie
nts
with
epile
psy
C
ontr
ols
Refr
act
ory
W
ell-
contr
olle
d
Base
line
At 6 y
ears
B
ase
line
At 6 y
ears
B
ase
line
(n=
18)
(n=
18)
(n=
18)
(n=
18)
(n=
36)
LF
(m
s x
ms)
733 (
545–1152)+
+
581 (
346–697)**
1043 (
653–1318)+
894 (
453–1266)
1520 (
865–2227)
HF
(m
s x
ms)
2
71
(1
87
–5
06
) 194 (
137–325) *
* 353 (
228–632)
251 (
141–603)
467 (
218–1329)
SD
1
21 (
19–29)+
19 (
15–23) *
* 24 (
22–32)
22 (
18–29)
29 (
20–47)
SD
2
112 (
102–133)+
+
98 (
90–107) *
* 120 (
104–140)+
104 (
90–143)
141 (
124–176)
α
1.2
6 (
1.1
7–1.3
1)
1.2
1 (
1.1
4–1.3
5)
1.2
5 (
1.1
9–1.3
7)
1.2
8 (
1.1
7–1.3
4)
1.2
7 (
1.0
8–1.3
9)
Ap
En
1.1
0 (
1.0
3–
1.2
0)
1.1
4 (
1.0
3–
1.2
9)
1.1
6 (
1.0
8–
1.2
6)
1.1
6 (
1.0
7–
1.3
1)
1.1
2 (
0.9
7–
1.2
4)
LF
, lo
w-f
requency
; H
F, hig
h-f
requency
; S
D1, beat-
to-b
eat va
riabili
ty m
ea
sure
fro
m P
oin
caré
; S
D2
, lo
ng
-te
rm v
aria
bili
ty m
ea
sure
fro
m P
oin
caré
; α
, sh
ort
-te
rm
fra
cta
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alu
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edia
ns
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and w
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LE
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.
67
68
Six patients with refractory TLE reported having experienced a partial seizure
during the 24-hour ECG recording at the baseline, and three patients with
refractory TLE reported having experienced a partial seizure during the second
24-hour ECG recording in Studies II-III. Analysis of HR variability of these
patients showed no differences compared with the analysis of HR variability in
the patients without seizures.
Altered HR variability was not associated with the AEDs used, with the
duration of TLE or the age of the patients. Furthermore, there was no correlation
between altered cardiac autonomic control and the lateralization of the seizure
focus (p >0.05) (Studies I-III).
5.3 Effect of vagus nerve stimulation on heart rate dynamics
(Study IV)
Seizure reduction of ≥50% (responder) was observed in nine patients (64%). The
non-responder group consisted of three patients who had experienced <50%
seizure reduction, and two patients who did not experience any change in seizure
frequency during VNS treatment.
The median value of the RR interval (p=0.008), SDNN (p<0.001), the
spectral components VLF (p<0.001), LF (p=0.001) and HF (p=0.002), and the
Poincaré components SD1 (p=0.005) and SD2 (p<0.001) of the patients with
refractory epilepsy were significantly lower than those of the control subjects
before VNS implantation. A similar decrease in HR variability measures was
found one year after VNS implantation (p<0.05) (Table 8).
There were no differences in any HR variability measurements in patients
with refractory epilepsy before and one year after the implantation of the VNS.
The median value of the RR interval (p=0.020), SDNN (p=0.045), VLF
(p=0.014), LF (p=0.014), HF (p=0.014), SD1 (p=0.009), SD2 (p=0.028) and the
power-law slope β (p=0.042) of the non-responders were significantly lower than
those of the responders one year after VNS implantation.
In study IV the high interindividual variability of documented seizures during
the 24-hour ECG recordings did not allow a meaningful statistical analysis of the
effects of seizures on HR variability. No significant arrhythmias were seen during
ECG recordings (study I-IV).
69
Table 8. Measures of HR variability in control subjects and in patients with refractory
epilepsy before and 1 year after the implantation of vagus nerve stimulator (Study IV).
Variables Control subjects Patients with epilepsy
Before VNS implantation One year after VNS
implantation
(n=28) (n=14) (n=14)
RRI (ms) 854 (809–922) 785 (722–826)** 750 (670–830)**
SDNN (ms) 181 (150–214) 127 (100–157)*** 116 (107–147)***
VLF (ms x ms) 3296 (1963–5604) 853 (609–1214)*** 984 (509–1918)***
LF (ms x ms) 1740 (1123–3275) 456 (327–1051)** 474 (179–694)***
HF (ms x ms) 1054 (315–2291) 232 (189–610)** 215 (166–396)***
SD1 34 (25–60) 21 (17–27)** 20 (17–26)***
SD2 143 (125–190) 94 (81–117)*** 90 (74–124)***
α 1.24 (1.06–1.30) 1.15 (1.08–1.17) 1.18 (1.09–1.23)
Slope β −1.14 (−1.37…−1.07) −1.28 (−1.40…−1.24) −1.33 (−1.45…−1.23)
RRI, RR interval; SDNN, SD of all NN intervals; VLF, very low-frequency; LF, low-frequency; HF, high-
frequency; SD1, beat-to-beat variability measure from Poincaré; SD2, long-term variability measure from
Poincaré; α, short-term fractal correlation parameter; slope β, long-term power-law slope. Values are
presented as medians (interquartile range).
**p<0.01, ***p<0.001 compared to control subjects, the Mann-Whitney U-test
71
6 Discussion
6.1 General aspects
Epilepsy is known to be associated with autonomic dysfunction during epileptic
seizures (Nei et al. 2000, Opherk et al. 2002), but there is also increasing
evidence of interictal ANS dysfunction in patients with chronic epilepsy
(Frysinger et al. 1993, Massetani et al. 1997, Isojärvi et al. 1998, Ansakorpi et al.
2000, Ansakorpi et al. 2002, Mukherjee et al. 2009). Previous studies have
analysed changes in HR variation by using short-term ECG recordings or by
analysing ECG recordings during 24-hour time period without analysing day and
night time separately, even though it is well documented that there are circadian
rhythms seen in many physiological events such as HR, thermoregulation,
wakefulness and sleep (Hastings et al. 2007). Furthermore, it is well documented
that there are characteristic diurnal patterns for cardiovascular events e.g. acute
myocardial infarction (Muller et al. 1985) and sudden cardiac death (Willich et al.
1987), as well as for epileptic seizures (Quigg et al. 1998, Hofstra et al. 2011).
Decreased circadian HR fluctuation has been reported in several
cardiovascular and neurological diseases (Bernardi et al. 1992, Chakko et al.
1993, Huikuri et al. 1994, Korpelainen et al. 1997, Pursiainen et al. 2002),
although the clinical significance of these findings has remained undefined. There
are no previous studies regarding changes in circadian HR variation in patients
with epilepsy. Moreover, there is no information about changes in long-term
cardiovascular autonomic regulation in patients with TLE assessed by HR
variability measurements in a longitudinal setting.
The present study was designed to evaluate long-term changes in HR
behaviour in patients with TLE. Special attention was also paid to analysing
changes in circadian HR variation in patients with well-controlled or refractory
TLE by measuring HR variability from 24-hour ambulatory ECG recordings
separately during the night and day time. With prospective follow-up studies the
focus of the research was also put on evaluating possible progressive changes in
cardiac regulation over time in patients with chronic TLE. Finally, the effects of
VNS treatment on cardiac regulation in patients with epilepsy were evaluated in a
prospective setting by analysing HR variation from 24-hour ECG recordings in
patients with chronic epilepsy prior to and one year after onset of VNS treatment.
The ultimate goal of this type of research is to identify HR variation
indicators that could in the future be used to predict the risk of SUDEP or other
72
untoward cardiac events in individual epilepsy patients. In this study, for the first
time, altered circadian cardiac autonomic control was observed in patients with
TLE, showing that the reduced HR variation is more pronounced during night
than during day time in these patients.
6.2 Clinical findings of autonomic nervous system function in
patients with epilepsy
It is well established that both partial and generalized epileptic seizures may be
accompanied by altered autonomic function characterized by multiple types of
symptoms and signs, such as cardiac arrhythmias, changes in blood pressure,
sweating, bowel and bladder dysfunction and incontinence, etc. These types of
symptoms and signs can occur between (interictally), during (ictally) and after
(postictally) epileptic seizures. In this study careful clinical evaluation of ANS
function (clinical examination and detailed, structured medical history using a
questionnaire) did not reveal any symptoms or signs of autonomic dysfunction,
even though altered regulation of cardiac function was detected by analysing HR
variation during a 24-hour period, and later in the analysis of circadian HR
behaviour in particular in the same patients, as described below (Studies I-IV).
6.3 Cardiac regulation in temporal lobe epilepsy
6.3.1 Long-term heart rate dynamics
This first long-term follow-up study on the effects of chronic TLE on HR
variability showed that the reduction in HR variability is progressive in chronic
refractory TLE whereas in patients who remained well-controlled with the
treatment no further decrease in HR variability was seen during the follow-up
(Study II). The findings of the present study are in accordance with results from
the previous studies showing reduced HR variability in patients with chronic TLE
(Massetani et al. 1997, Ansakorpi et al. 2002, Hilz et al. 2002).
Significant reductions of HR variability were observed in quantitative
measures of Poincaré plots. SD2 mainly describes long-term HR variability and
SD1 short-term variability. The former reflects partly physical activity of the
patients in addition to autonomic regulation of HR (Tulppo & Huikuri 2004). The
latter index is a more direct measure of cardiac vagal outflow. In fact, SD1 is a
more reliable index of cardiac vagal activity than the high-frequency spectral
73
component of HR variability, when measured from ambulatory recordings
(Tulppo et al. 1998). The present results suggest that refractory TLE is mainly
related to progressive diminution of cardiac vagal outflow. It is also possible that
physical daily activity is reduced in refractory TLE during the time course, partly
explaining the marked reduction in SD2 index of Poincaré plots.
In TLE seizures arise from mesial temporal structures, and damage in those
areas may result in abnormalities manifesting as attenuation of HR variation. It
has been proposed that decrease in HR variability is partly due to functional
rather than structural changes in TLE (Ansakorpi et al. 2004). One study (Persson
et al. 2006) where HR variation was studied in patients with epilepsy surgery
suggested that there may be a pre-existing biologic difference between patients
who become seizure-free after surgery and those who do not. Furthermore, it
seems that the anterior part of the temporal lobe does not play a major role in the
circadian regulation of HR variability. On the contrary, one recent study
suggested that hippocampal sclerosis may be associated with autonomic
dysfunction in patients with TLE (Koseoglu et al. 2009). The design of the
present study did not allow drawing any conclusions about the possible
association between the damage to specific temporal lobe structures and the
observed changes in autonomic cardiovascular control.
HR variability has been shown to decrease with normal ageing in healthy
people. It is typically lower in elderly people compared to middle-aged or young
subjects, and these age-related changes seem to be modified by gender (Hayano et
al. 1991, Pikkujämsä et al. 1999, Fukusaki et al. 2000, Jokinen et al. 2005).
Therefore, ageing may also have had an effect on the present longitudinal results.
However, after the six-year follow-up the observed changes in the HR variation
were more evident in patients with refractory TLE than in patients with well-
controlled TLE after the follow-up, which suggests that other reasons than ageing
were more important in contributing to the observed changes in the HR variation
in the present study.
6.3.2 Circadian heart rate variation
The literature concerning circadian HR variation in epilepsy is scarce. There is
only one study evaluating cardiac autonomic function during night time in
children with partial epilepsy (Ferri et al. 2002). In that study, it was
demonstrated that during sleep, patients with epilepsy tended to have overall
lower HR variability in both time- and frequency domain parameters. Similar
74
dysfunction in autonomic cardiac regulation during the night time was observed
in patients with TLE in the present study. However, day time values were also
decreased in the present patients when the values were compared to healthy
control subjects (Study I). The lower values in HR variability measurements were
seen especially in the LF and HF power spectral components and SD1 and SD2
measures of Poincaré analysis when the values of patients with TLE were
compared to those of control subjects. In accordance with the present studies,
Persson et al. reported a similar non-specific disturbed autonomic cardiac
function during the night time that was not present in newly diagnosed
localization-related epilepsy until CBZ treatment was started (Persson et al.
2007).
The present finding of suppressed circadian HR variation, especially during
the night, is of particular interest in relation to SUDEP. Many SUDEP cases seem
to share some similar features related to the circumstances of death. Patients are
often found in bed or are known to have been asleep before death (Leestma et al.
1989, Nashef et al. 1996, Kloster & Engelskjøn 1999, Nei et al. 2004, Nobili et
al. 2010) suggesting that the risk for specific aetiologic mechanisms directly
responsible for death may increase during sleep, and sleep-related seizures could
differ pathophysiologically (Tomson et al. 2008). Of interest, the present results
showed that suppression of HR variability in TLE is most pronounced at night,
suggesting a night time parasympathetic dysfunction for patients with epilepsy.
Furthermore, these results suggest that reduced HR variability may be one
contributing mechanism by which patients with refractory TLE are subject to an
increased risk of SUDEP. In the current study, both patients with refractory and
well-controlled TLE had diminished circadian HR variation, while the observed
changes were more evident in patients with refractory TLE. The present findings
support the view that patients with recurrent seizures are at greater risk for
dysfunction of autonomic cardiac regulation (Ansakorpi et al. 2000, Ansakorpi et
al. 2002, Persson et al. 2005, Mukherjee et al. 2009). However, the present
observation that suppressed circadian HR variation was also present in patients
with well-controlled TLE indicates that cardiac regulation is also altered in
patients whose epilepsy seems to be well managed and who do not present with
seizures while being treated. Consistent with this it is well known that SUDEP
may also occur in patients with well-controlled seizures (Harvey et al. 1993,
Racoosin et al. 2001, Langan et al. 2005).
To our knowledge, for the first-time, the present study showed that patients
with TLE have altered circadian HR variation with more pronounced decrease in
75
HR variation during the night than during the day. Furthermore, the nocturnal
increase in HR variation usually seen in the normal population could not be
detected in patients with refractory epilepsy (Studies I,IV).
6.3.3 Long-term changes in circadian heart rate variation
In this long term follow-up study, the present results showed that patients with
refractory TLE with recurrent seizures had progressive diminution of the HR
variation both in day and night time (Study III). Especially the LF and HF power
spectrum components of HR variability and the SD1 and SD2 of the Poincaré
analysis were further decreased, implicating extensive disruption of the
autonomic control of the heart. The present results are in accordance with
previous studies showing that chronic epilepsy affects the autonomic cardiac
regulation system and that both the sympathetic and parasympathetic system are
affected (Frysinger et al. 1993, Massetani et al. 1997, Isojärvi et al. 1998, Tomson
et al. 1998, Ansakorpi et al. 2000, Harnod et al. 2008, Ansakorpi et al. 2011)
Patients with refractory epilepsy are thought be at greater risk for SUDEP
(Tomson et al. 2008). More attention has also been paid to possible association of
altered cardiovascular autonomic regulation with the pathogenesis of SUDEP
(Nashef et al. 1996, Nei et al. 2000). One recent study suggested that although
reduced HR variation is not considered a direct indication of elevated risk of
SUDEP, the inferior capacity of the ANS to react during dynamic situations may
be an indicator of potential autonomic imbalance during generalized epileptiform
seizures (Ansakorpi et al. 2011). Of interest, the present study showed that the
HR variation is progressive in patients with refractory TLE with recurrent
seizures, although the circadian HR dynamics do not seem to change further with
longer duration of chronic TLE. These results suggest that in patients with
epilepsy reduced circadian HR variation may reflect refractoriness of the
condition, and increased risk of SUDEP. Furthermore, it seems that suppression
of circadian HR variability, especially during the night, may occur early on in
epilepsy. Analysis of circadian HR variation from 24-hour ECG recordings adds
value to the evaluation of HR variability in patients with epilepsy, and may
eventually help in identifying patients at greater risk for dysfunction of autonomic
regulation of HR with possible undesirable outcomes, especially SUDEP.
76
6.3.4 Effect of antiepileptic medication on heart rate variation
The possible role of AEDs in altering HR variation is interesting, but the effect of
different AEDs on HR variability is difficult to distinguish from that of the
epilepsy itself. AEDs have direct effects on the cardiac conduction system, e.g. by
blocking sodium channels, but they may also act indirectly through a central
effect mediated by autonomic nervous control of the heart. Previous studies
suggest that CBZ therapy may be associated with decreased HR variability
(Devinsky et al. 1994, Isojärvi et al. 1998, Tomson et al. 1998, Ansakorpi et al.
2000, Persson et al. 2003), and one study in patients with medically intractable
epilepsy reported increased cardiac sympathetic activity during sleep induced by
sudden discontinuation of CBZ (Hennessy et al. 2001). Furthermore, CBZ
treatment has also been suggested to be associated with an increased risk of
SUDEP, but the reports are controversial (Kennebäck et al. 1997, Timmings 1998,
Nilsson et al. 1999, Walczak et al. 2001, Hesdorffer et al. 2011). In this regard,
LTG has also been suggested to be associated with an increased risk of SUDEP in
patients with idiopathic epilepsy (Aurlien et al. 2007). This finding is supported
by a pooled analysis of previous case control studies, in which LTG therapy was
associated with significantly increased risk for SUDEP (Hesdorffer et al. 2011).
In the present study, no correlation was found between altered HR variability
and any particular AEDs used. Nor were any clinically significant cardiac
arrhythmias observed in the study population. However, all present patients with
refractory TLE and the majority of patients with well-controlled TLE were taking
a voltage-dependent sodium channel blocker with or without other AEDs. Patients
with refractory TLE were also more often on polytherapy while well-controlled
patients were on monotherapy. (Studies I-III) Therefore, it is not possible to
assess individual effects of different types of AEDs on HR variation in this
population. Small sample size also reduces the likelihood of being able to show
statistically significant differences between the treatment groups in relation to
HR. Therefore, the possible effect of AEDs on HR variation needs to be further
evaluated in larger patient populations in the future.
6.4 Effect of vagus nerve stimulation on heart rate dynamics
The results of previous studies on possible effect of VNS treatment on cardiac
autonomic function are controversial. One study suggested that VNS treatment
may have some effect on cardiac autonomic function with a reduction of the HF
77
component during the night and a flattening of sympathovagal circadian changes
(Galli et al. 2003). On the other hand, other short-term studies have suggested
that VNS does not have an effect on the HR (Handforth et al. 1998, Setty et al.
1998) or cardiac rhythm during sleep (Murray et al. 2001).
The present results suggest that one-year treatment with VNS does not have a
marked effect on HR variability (Study IV). Interestingly, one year after the
implantation of VNS, most of the patients had experienced seizure reduction by
more than 50%, but this reduction was not associated with changes in HR
variability in the patient group as a whole. It was also found that there were no
differences in HR variation between subjects who turned out to be responders and
subjects who turned out to be non-responders to the VNS treatment in the analysis
of the ECG recordings that were obtained prior to the VNS implantation. The
present results suggest that long-term VNS treatment does not affect autonomic
nervous system function as reflected by HR variability. These result are also
consistent with findings suggesting that epilepsy may itself alter HR variation in
the long term independently of seizure recurrence (Ansakorpi et al. 2004)
VNS treatment has been shown to be safe, well tolerated and effective in
seizure reduction (Amar et al. 1999, Schachter 2004, Wheeler et al. 2011).
Consistent with these findings, in the present study VNS treatment was well
tolerated without major adverse effects and most of the patients experienced more
than 50% seizure reduction during the first year of treatment.
6.5 Methodological considerations
The individual studies were designed carefully, but there are certain limitations in
the design that may have influenced the results and need to be considered when
interpreting the current findings.
The number of participants in each individual study was small. Due to the
small number of subjects, the applicable statistical analysis method options
available were limited. It is also possible that some differences in various
comparisons that were not statistically significant in the analyses might have
reached statistical significance if a larger number of subjects had been available
for these comparisons. In addition, there was no control group in Study III for
prospective evaluation, and information about possible prospective changes in
HR variation in a non-epileptic subjects was therefore not available for
comparison.
78
Physical activity is known to increase HR variability. However, the design of
the present study did not allow analysis of the effect of physical activity on HR
variability in the study subjects.
In summary, larger studies are needed to confirm the present findings and to
establish the utility of these HR variability measurements for routine use.
79
7 Conclusions
1. HR variation is reduced in patients with chronic TLE, indicating an altered
cardiovascular regulation in these subjects. The reduction in HR variation is
more pronounced in subjects with refractory TLE than in subjects with well-
controlled TLE.
2. HR variation further decreases with time in refractory TLE, but not in well-
controlled TLE.
3. The circadian HR dynamics alter in TLE, i.e. the decrease in HR variation is
more evident during the night than during the day. This alteration in circadian
HR dynamics is evident both in patients with refractory TLE and in subjects
with well-controlled TLE, but it is more pronounced in subjects with
refractory TLE.
4. Even though the HR variation decreases with longer duration of refractory
TLE, the altered circadian HR dynamics do not seem to change further with
longer duration of chronic TLE.
5. Despite the important role of the vagal nerve in the regulation of HR, it seems
that VNS treatment for chronic epilepsy does not have a significant impact on
cardiovascular function evaluated by HR variation in 24-hour ECG
recordings.
81
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Original publications
I Ronkainen E, Ansakorpi H, Huikuri HV, Myllylä VV, Isojärvi JIT & Korpelainen JT (2005) Suppressed circadian heart rate dynamics in temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 76(10): 1382–1386.
II Suorsa E, Korpelainen JT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Isojärvi JIT (2011) Heart rate dynamics in temporal lobe epilepsy – a long term follow-up study. Epilepsy Research 93(1): 80–83.
III Suorsa E, Isojärvi JIT, Ansakorpi H, Huikuri HV, Suorsa V, Myllylä VV & Korpelainen JT (2011) Long-term changes in circadian heart rate variability in patients with temporal lobe epilepsy. Manuscript
IV Ronkainen E, Korpelainen JT, Heikkinen E, Myllylä VV, Huikuri HV & Isojärvi JIT (2006) Cardiac autonomic control in patients with refractory epilepsy before and during vagus nerve stimulation treatment – a one year follow-up study. Epilepsia 47(3): 556–562.
Reprinted with permission from BMJ Group (I), Elsevier (II) and Blackwell
Publishing, Inc. (IV).
Original publications are not included in the electronic version of the dissertation.
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ASSESSMENT OF HEART RATE VARIABILITY AS AN INDICATOR OF CARDIOVASCULAR AUTONOMIC DYSREGULATION IN SUBJECTS WITH CHRONIC EPILEPSY
UNIVERSITY OF OULU,FACULTY OF MEDICINE,INSTITUTE OF CLINICAL MEDICINE,DEPARTMENT OF NEUROLOGY