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Eija Suorsa

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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

To my family

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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

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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

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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).

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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)

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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

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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.

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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

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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

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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).

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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.

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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

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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,

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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.

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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).

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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

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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

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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).

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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 i