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Effect of Ekagrata Meditation on Sympatho-Vagal
Balance-An Auto-Regressive Model Approach
Ramesh K. Sunkaria Department of Electronics & Comm. Engg., Dr. B R Ambedkar National Institute of Technology,
Jalandhar (Punjab), India
Email: [email protected]
Abstract—The ekagrata meditation state pertains to
channelizing the thoughts in one direction. In the present
work the effect of meditation state of ekagrata on heart rate
variability dynamics have been analyzed. For this, the
electrocardiogram was recorded in thirty (n=30)
experienced male yoga practitioners (mean age 32 years,
range 22-38 years, mean height 164 cms) in pre, during
and post-sessions of ekagrata meditation. The heart rate
variability was evaluated using optimized autoregressive
technique. The sympatho-vagal balance (LF/HF ratio)
became sympathetic tone (LF power) dominant, while vagal
tone (HF power) was observed to be significantly low
(p<0.01) under meditation state of ekagrata. The total
variability (Ptotal (ms2)) is significantly high (p<0.05) and
mean heart rate (HR (bpm)) is lowered under this
meditation state.
Index Terms—heart rate variability, ekagrata meditation,
optimized AR model, LF power, HF power.
I. INTRODUCTION
The previous studies on yoga therapy, breathing
exercises and meditation have demonstrated their
therapeutic potential to influence the autonomic nervous
system dynamics. Howorka K. et al. (1995) demonstrated
an immediate decrease of sympathetic and increase of
parasympathetic activity after yoga in a preliminary study
[1]. The heart rate variability study by Raghuraj P. et al.
(1998) in two selected breathing techniques, viz.
Kapalabhati (breathing at high frequency) and nadisudhi
(alternate nostril breathing) exhibited significant increase
in low frequency (LF) power and LF/HF ratio while high
frequency (HF) power was significantly lowered
following kapalabhati, but there were no significant
changes following nadisuddhi [2]. Peng C. K. et al. (1999)
reported extremely prominent heart rate oscillations
during Chinese Chi and Kundalini yoga meditation
techniques with slow breathing, which challenged the
very notion of meditation as only an autonomically
quiescent state [3]. Peng C. K. et al. (2003) in their later
study on heart rate variability in three forms of meditation
observed increased coherence between heart rate and
breathing during relaxation response and segmented
breathing when compared to baseline. The third form of
meditation i.e. breath of fire was marked by increase in
Manuscript received April 6, 2013; revised May 8, 2013.
mean heart rate with respect to baseline and a significant
decrease in coherence between heart rate and breathing
[4]. Vladimir S. and Ofer B. (2005) emphasized the
sympathetic nervous system dynamics to counter mental
stress and other external or internal disturbances effecting
visceral homeostasis [5]. Jovanov E. (2005) during very
slow yogic breathing found increased VLF frequencies,
respiratory sinus arrhythmia, LF/HF ratio and decreased
breathing frequency after exercise in comparison to state
before exercise [6]. Patil S. and Telles S. (2006) in their
study on two yogic based relaxation effects on HRV in
cyclic meditation (CM) and supine rest (SR), showed
decrease in LF power and LF/HF ratio during and after
CM, whereas HF power decreased. The heart rate
increased during CM but decreased after CM. However,
there was no change in SR state [7]. Travis F. et al. (2008)
tested the advantageous effect of transcendental
Meditation (TM) technique in overcoming stress in
college students [8]. Sunkaria et al. (2010) observed high
HRV in yogic practitioners in comparison to non-yogic
practitioners [9].
The present study evaluates heart rate variability
dynamics during meditation states of ekagrata. The effect
of this meditation state on autonomic control dynamics
may enhance the therapeutic utility in relevant cardiac
health conditions.
II. METHODS
A. Subjects
Thirty four experimental volunteers for
electrocardiogram (ECG) recording were selected from
Swami Vivekanand Yoga Research Foundation,
Bangalore, India. The selected volunteers were regular
yoga practitioners having 3-12 twelve years of meditation
experience and were used to practice 3-5 times/week.
They were in excellent health condition and were not
having even history of any cardiac or vascular disease.
This was also ascertained by a thorough medical
examination. The subjects were explained the
experimental procedures and that the recorded ECG
signal would be used for academic and research purpose
alone. An informed written consent was obtained from
each subject prior to recording. The lead-II ECG was
recorded in each of the thirty four male volunteers having
age in the range of 22-to -38 years (mean age 32 years,
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mean height 164 cms) under ekagrata state. The signal
quality in four recorded signals was contaminated by
artifacts especially due to subject’s movements, so in
total, good quality ECG data of thirty subjects were used
for analysis.
B. Inclusion/Exclusion Criteria
The normal and healthy yoga practitioners were
included in the present study after a thorough medical
examination and fulfilling certain physiological
requirements. Subjects having pulse rate > 100 bpm,
blood pressure > 160/100 and subjects suffering from
severe breathlessness were left out of the recording
process. The ECGs were checked and none of the
subjects had extra systoles.
C. Intervention of Ekagrata Meditation State and
Recording Protocols
Each of the selected yoga practitioners underwent ECG
recording protocol as per Table I under ekagrata state of
meditation with total duration of more than thirty minutes
comprising of pre, during (D1, D2, D3 and D4) and post
session with conditioning gaps. The interventional
meditation states of ekagrata are channelizing the
thoughts in one direction. All recording sessions of pre,
D1, D2, D3, D4 and post were of duration five minutes
each. The recording was made on subsequent days
between 05:00 and 08.30 hours in the morning or 05:00
and 08:30 hours in the evening to avoid outside
disturbance to cover all the subjects. The instructions to
all subjects were as per given in the Table I.
TABLE I. THE ECG DATA RECORDING PROTOCOLS
Sessions ekagrata
Pre (5 min) Sit and relax
Gap (2 min) Viewing slides of pictures pertaining to
mountains, rivers etc. with logical linking among pictures.
D1 (5 min) Play of logical lecture on cyclic meditation
D2 (5 min) Play of logical lecture on cyclic meditation
D3 (5 min) Play of logical lecture on cyclic meditation
D4 (5 min) Play of logical lecture on cyclic meditation
Post (5 min) Sit and relax
D1-D4 are sessions of interventional ekagrata meditation state
D. Data Acquisition and Analysis
The lead-II ECG signal of each subject was recorded at
sampling frequency of 250 Hz using 4-channel RMS
polyrite ECG recorder (Chandigarh, India) in all sessions
as elaborated above. The acquired ECG data was visually
inspected and only visibly artifact free data were used.
The ECG signal was filtered using FIR band-pass (0.5
Hz-100 Hz) filter to remove any high frequency noise and
base wander, while retaining the diagnostically useful
frequency range. The R-peaks were detected using highly
efficient new wavelet transform function based QRS
detection algorithm [10]-[12]. This resulted into highly
accurate RR-tachogram (HRV signal), which leads to
evaluation of highly reliable HRV indices.
E. Heart Rate Variability Measurement
1) Spectral domain HRV indices using optimized AR
model
The heart rate variability evaluated using FFT with
improved windowing technique gives highly accurate
spectral HRV indices magnitudes as window bias caused
by windowing of data is removed in this algorithm. So
HRV indices with this technique have been taken as
reference. The AR modal has smooth PSD curve and easy
identification of central frequencies in spectral bands
corresponding to intrinsic oscillators. Moreover, the
spectral resolution in case of AR technique is mostly
affected by order of the model because time resolution is
independent of frequency resolution, so even a shorter
window length can be used. Further, the order of model
affects the spectral band (VLF, LF & HF) powers and
other HRV indices. So, a criterion is chosen for model
order selection, such that variance of the prediction error
is minimized. The Akaike’s FPE (finite prediction error)
criteria for model order selection has the disadvantage
that magnitude of HRV indices differs from those
evaluated with fast Fourier transform (FFT) technique
[13]. In the present study, this model order has been
optimized and evaluated on the basis of total prediction
error (TPE). It was observed experimentally, that if the
highest order of model is evaluated corresponding to total
prediction error (TPE) of +1% more than least square
error computed with FPE criteria or till the trend of least
square error starts increasing, then this model order
provides HRV indices which are closely same to those
evaluated with FFT technique, without sacrificing the
inherent advantages of AR model. However, if model
order is evaluated corresponding to more than this +1%
of least square error, then the HRV indices again starts
deviating from those evaluated with FFT [14]-[17].
The R-peaks were detected using highly efficient
detection algorithm and RR-interval tachogram was
formed. This RR-tachogram was resampled at 4 Hz and
HRV indices were evaluated using above elaborated
optimized autoregressive (AR) model for all four
meditation states in their pre-during-post sessions. Three
frequency bands were considered as per the task force
guidelines: very low frequency (VLF, 0.04 Hz), low
frequency (LF, 0.04Hz-0.15Hz) and high frequency (HF,
0.15Hz-0.40Hz). The spectral band powers were
evaluated by summation of power in each of these
frequency bands. The physiological processes attributed
to the VLF band are not well defined, but it is believed to
be associated with thermoregulatory mechanisms and
rennin-angiotensin variations. LF signifies sympathetic
tone and parasympathetic tone is related to the HF. The
VLF, LF and HF power components may be computed in
absolute (millisecond square) or normalized units (n.u.).
The LFn.u. HFn.u. indicates proportion of autonomic
control by sympathetic and parasympathetic branches of
autonomic nervous system [18]-[21]. The time domain
HRV measures of mean heart rate (HR (bpm)) was also
evaluated from RR- interval series in the present study for
further supporting the spectral based analysis. The mean
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©2013 Engineering and Technology Publishing
heart rate indicates the heart rate dynamics in the
meditation state.
2) Statistical analysis
The group values of various HRV indices for all four
meditation states are summarized as mean S.D. in pre,
during and post sessions in Table II. Since, the recordings
were made on subsequent days, so pre-session values of
HRV indices differs slightly under all four states.
Therefore each pre-session HRV indices values have
been taken as baseline and their percentage change in
magnitude during ekagrata meditation state and in post-
sessions with pre-session values as baseline indicates the
effect of ekagrata meditation state. The significance of
change during and in post-sessions were determined by
one way ANOVA test with pre, during and post session
group values formulating group values. The ‘p-values’
and ‘Fisher F-values’ determined by one way ANOVA
test have been quoted in Table II.
TABLE II. HEART RATE VARIABILITY DYNAMICS IN PRE-AND DURING EKAGRATA MEDITATION
HRV
measure
aSubjects
(n)
bBaseline
(Pre-session)
cDuring ekagrata
meditation state Post-session F p
LF (ms2) 30 348.59 132.57 540.92 264.39 612.68 297.30 9.54 0.000
HF (ms2) 30 289.42 55.25 251.39 67.79 253.63 66.22 3.40 0.038
LF/HF ratio 30 1.89 1.69 2.57 1.22 2.96 1.64 3.75 0.027
LFn.u. 30 58.13 14.91 68.43 9.40 70.36 12.32 8.41 0.000
HFn.u. 30 41.87 14.91 31.57 9.40 29.64 12.32 8.41 0.000
Ptotal (ms2) 30 1010.72 448.59 1353.73 557.69 1298.03 510.70 3.94 0.023
HR (bpm) 30 77.47 7.45 75.52 6.93 75.94 6.43 0.65 0.522
LF = power in low frequency; HF = power in high frequency; ms2=absolute units; n.u.= normalized units; LF/HF ratio = sympatho-vagal
balance; Ptotal=total power; HR= mean heart rate. aHRV indices in terms of mean and standard deviation calculated for all thirty subjects (n=30) participating in the study in pre,during and post
sessions of ekagrata meditation state. bPre-session values were considered as baseline.
cValues were averaged over D1-D4 sessions of meditation states.
III. RESULTS
The typical effect of ekagrata meditation state on heart
rate variability dynamics is shown in Fig. 1 for one of the
representative subject in pre and during ekagrata
meditation state for one of the session (D4). The result for
session D4 has been shown as the subject will effectively
transit into ekagrata meditation by the session.
0 50 100 150 200500
600
700
800
900
1000
1100
Beat number
RR
-in
terv
al (
ms)
RR-interval series in pre-session
(a)
0 0.1 0.2 0.3 0.4 0.50
0.2
0.4
0.6
0.8
Frequency (Hz)
PS
D (
*1
03*
ms2
)
PSD in pre-session
(b)
0 50 100 150 200500
600
700
800
900
1000
1100
Beat number
RR
-in
terv
al(m
s)
RR-interval series during ekagrata
(c)
0 0.1 0.2 0.3 0.4 0.50
0.05
0.1
0.15
0.2
Frequency (Hz)
PS
D (
*1
03*
ms2
)
PSD during ekagrata
(d)
Figure 1. Heart rate variability of a representative subject in pre-and
during one of the ekagrata session (D4). The power spectral density (PSD) (Fig. 1(b) and 1(d)) in pre-and during ekagrata meditation state
are shown alongside with their with their corresponding RR-interval series (Fig. 1(a) and 1(c)). The RR-interval series and their PSD curve
are shown for session D4 such that the effect of meditation states is
clearly observable. Only 200 numbers of RR-intervals are shown in above figure for clear visibility, although these are more in any 5-
minutes duration session.
A. Comparison of HRV in Pre- and During Ekagrata
Meditation State
To determine the mean effect of ekagrata meditation
state on heart rate variability dynamics the mean value of
HRV indices during multiple sessions of D1, D2, D3 and
D4 have been calculated for all thirty (n=30) subjects.
The quantitative results for these thirty subjects (n=30)
showing effect of ekagrata on heart rate variability
dynamics are summarized in Table II. The results are
presented in terms of mean and standard deviation (for all
thirty subjects) in pre, during and post sessions. The
variations in frequency domain HRV indices in pre- and
during ekagrata states are either significant or very
significant. However the time domain indices of rMSSD
(ms) do not show the consistency in results.
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©2013 Engineering and Technology Publishing
B. Comparison of Ekagrata Meditation States with
Baseline
The HRV indices during and in post-sessions were
compared with their baseline (pre-session) values. The
indices were differently affected and their magnitude of
variations in comparison to the baseline is shown in Table
III in terms of percentage change with respect to baseline
value.
TABLE III. PERCENTAGE CHANGE IN HRV INDICES IN REFERENCE TO
BASELINE (PRE-SESSION) DURING EKAGRATA MEDITATION STATE AND
IN ITS POST-SESSION (N = 30)
Variables During ekagrata meditation Post-session
LF (ms2) 55.17 75.76
HF (ms2) -13.14 -12.36
LF/HF ratio 35.92 56.61
LFn.u. 18.92 21.02
HFn.u. -24.61 -29.22
Ptotal (ms2) 33.91 28.4
HR (bpm)
-2.50 -1.91
+ve value = increase in reference to pre-session; -ve value = decrease in
reference to pre-session *Percentage change was calculated for change in mean value (for thirty
subjects) of particular HRV indices during and in its post-session in
reference to baseline.
Compared to baseline, the percentage increase in LF
power was very significantly higher during ekagrata
(p<0.01) and this persists even in post-session. The HF
power is lowered very significantly (p<0.01) during
ekagrata and it remains lowered in post- ekagrata session.
The autonomic balance (LF/HF Ratio) is sympathetic
tone dominant during and even in post-ekagrata session.
The normalized LF and HF have shown the same trend as
the absolute LF and HF does. The total power (Ptatal
(ms2)) increases very significantly during ekagrata
(p<0.05). The mean heart rate (HR (bpm)) is lowered
during ekagrata and remains lowered in post-session.
IV. DISCUSSIONS
The meditation state of ekagrata used as an
intervention for the present study is one of the meditation
states towards the path of attaining final state of Samadhi
as per yoga literature. The results pertaining to heart rate
variability dynamics during this meditation state has
revealed interesting autonomic control dynamics as
observed in Table II. The quantitative variations in HRV
measures in reference to baseline during and in post-
sessions shown in Table III comprehend the effect of
interventions. It was observed that there is increased
power in LF during ekagrata, which indicates that
sympathetic tone is markedly enhanced under ekagrata.
The vagal tone is markedly reduced under ekagrata.
This is indicator of the fact that heart rate variability is
reduced under ekagrata. The sympatho-vagal balance
(LF/HF ratio) is sympathetic tone dominant, thus
autonomic control become more responsive to peripheral
variations i.e. meditation state of ekagrata. The
normalized indices of LF and HF show the percentage
contribution of sympathetic and vagal tones in autonomic
control. The reduced mean heart rate (HR (bpm)) in
ekagrata is similar to other studies undertaken previously.
The above trends of various heart rate variability
dynamics related measures, such as sympathetic tone
(LF), sympatho-vagal balance (LF/HF ratio), total
variability (Ptotal (ms2)), normalized LF, HF sustains
even in their post-sessions.
This study assessed effect of meditation state of
ekagrata on heart rate variability in selected middle aged
male volunteers, who were having training in meditation
practices. Inclusion of female subjects would have added
wider applicability of the study. However, the subjects
taking part in the present study were regular yoga
practitioners and taking pre-session values of each HRV
measure as baseline or control is well justified as the
trained yoga practitioner has the ability to be in particular
meditation state at a given time. Future studies may help
to elucidate effect of ‘Om’ meditation over other forms of
meditation and how the autonomic control is regulated.
V. CONCLUSIONS
This study reveals very interesting autonomic control
dynamics under intervention of ekagrata meditation state.
The sympathetic tone is increased, where as the vagal
tone is reduced under ekagrata. The increased sympatho-
vagal balance under this meditation state further supports
this fact. Thus, the heart rate variability is reduced under
ekagrata meditation state. The increased total spectral
power Ptotal (ms2) may be due to increased visceral
dynamics due to interventional meditation state.
ACKNOWLEDGMENT
The authors are highly thankful to Department of
Electronics & Communication Engineering and Director,
Dr. B.R. Ambedkar National Institute of Technology,
Jalandhar for providing all necessary help to carry out the
present work. The authors are also highly thankful to
Electrical Engineering Department and administration of
Institute of Technology Roorkee, India for their all
administrative and technical assistance in providing the
various facilities for this work. The help in clinical
aspects of Yoga and interpretation of results in this study
rendered by research and support staff of Yoga Research
Centre at Patanjali Yogpeeth, Haridwar, India is highly
appreciated.
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Ramesh Kumar Sunkaria was born in Kot Khalsa, Amritsar (Punjab),
on February 20, 1967. He received his B.Tech. degree in Electronics Engg from GNDU, Amritsar and M.Tech. (Hons.) degree in Electronics
and Communication Engg. from GNDEC, Ludhiana in 1990, and 2004
respectively. He received his Ph.D. degree from Electrical Engineering Department, IIT Roorkee in June, 2010 under the guidance of Dr. Vinod
Kumar, Professor and Head, Electrical Engineering Department, IIT Roorkee, Dr. Suresh C. Saxena, Director, IIT Roorkee and Dr. Achala
M. Singhal, Professor and Head, Cardiology Department, HIHT,
Dehradun (Uttrakhand). He has worked as Engineer in BHEL, Haridwar upto 1994 and Assistant Director (Engg.) in IBES, Govt. of India till
August 1996. At present he is Assistant Professor in Department of Electronics & Communication Engineering, Dr. B. R. Ambedkar
National Institute of Technology, where he has been teaching UG/PG
courses related to Biomedical Signal/Image Processing and Digital Signal Processing and Its Applications. His professional research
interests are in Medical Signal/Image Processing, Heart Rate Variability Signal Analysis and Digital Signal Processing.
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