Heart Rate Oscillations during Meditation 1.0.0

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<strong>Exaggerated Heart Rate Oscillations During Two Meditation Techniques</strong>

C.-K. Peng, PhD, Joseph E. Mietus, BS, Yanhui Liu, MSc,
Gurucharan Khalsa, PhD, Pamela S. Douglas, MD,
Herbert Benson, MD, Ary L. Goldberger, MD
<p style="width: 80%; margin: 0 auto;">
From the Margret &amp; H.A. Rey Laboratory for Nonlinear Dynamics in
Medicine, Cardiovascular Division, and the Mind/Body Medical Institute
of CareGroup, the Beth Israel Deaconess Medical Center, Boston,
Massachusetts; Harvard Medical School, Boston, Massachusetts; Center
for Polymer Studies and Department of Physics, Boston University,
Boston, Massachusetts.</p>

<div class="notice">
This article originally appeared in the <I><a
Journal of Cardiology</a></I> <B>70:</B> 101-107, 1999.  Please cite
this publication when referencing this material. Presentation of this
study and the supporting data has been made possible by grants from the
<a href="http://www.fetzer.org/" target="other">Fetzer Institute</a>, <a
href="http://www.nasa.gov/" target="other">National Aeronautics and Space
Administration</a>, <a href="http://www.nimh.nih.gov/" target="other">National
Institute of Mental Health</a>, and The G. Harold and Leila Y. Mathers
Charitable Foundation.  <a href="meditation.ps">PostScript</a> and <a
href="meditation.pdf">PDF</a> versions are also available.  The cardiac
inter-beat interval data on which this article is based may be found <a


We report extremely prominent heart rate oscillations associated with
slow breathing during specific traditional forms of Chinese Chi and
Kundalini Yoga meditation techniques in healthy young adults. We
applied both spectral analysis and a novel analytic technique based on
the Hilbert transform to quantify these heart rate dynamics.  The
amplitude of these oscillations during meditation was significantly
greater than in the pre-meditation control state and also in three
non-meditation control groups: i) elite athletes during sleep, ii)
healthy young adults during metronomic breathing, and iii) healthy
young adults during spontaneous nocturnal breathing. This finding,
along with the marked variability of the beat-to-beat heart rate
dynamics during such profound meditative states, challenges the notion
of meditation as only an autonomically quiescent state.</p>


There has been much interest in heart rate dynamics during a variety
of physiological and pathological states. In addition, considerable
attention has been focused on the potential health benefits of a
variety of meditative, relaxation techniques and their possible
effects on neuroautonomic function. Surprisingly, however, there is
little information regarding the effects of meditation on beat-to-beat
heart rate dynamics as an indirect ``assay'' of autonomic regulation
 href="#sakakibara94">3</a>]. Accordingly, we collected and
analyzed continuous heart rate time series from two groups of healthy
young adults before and during two well-known forms of meditation.  We
sought to determine 1) whether there are any distinctive heart rate
dynamics during these practices, and 2) whether such meditative states
induce a quiescent (less variable) or active (more variable) pattern
of autonomic response.</p>

<h2>Materials and Methods</h2>

<h3>Subjects and Meditation Protocols</h3>

Two specific meditative techniques were studied: (i) Chinese Chi (or
Qigong) meditation (as taught by Xin Yan) and (ii) Kundalini Yoga
meditation (as taught by Yogi Bhajan).</p>

The Chi meditators were all graduate and post-doctoral students. They
were also relative novices in their practice of Chi meditation, most
of them having begun their meditation practice about 1-3 months
before this study. The Kundalini Yoga subjects were considered to be at
an advanced level of meditation training.  The subjects of both
meditation groups were in good general health and did not follow any
specific exercise routines. All subjects provided informed consent in
accord with a protocol approved by the Beth Israel Deaconess Medical
Center Institutional Review Board.</p>

The eight Chi meditators, 5 women and 3 men (age range 26-35, mean 29
yrs), wore a Holter recorder for approximately 10 hours during which
time they went about their ordinary daily activities.  At
approximately 5 hours into the recording they each practiced one hour
of meditation. Meditation beginning and ending times were delineated
with event marks.</p>

During these sessions, the Chi meditators sat quietly, listening to
the taped guidance of the Master. The meditators were instructed to
breath spontaneously while visualizing the opening and closing of a
perfect lotus in the stomach. The meditation session lasted about one

The four Kundalini Yoga meditators, 2 women and 2 men (age range
20&ndash;52, mean 33 yrs), wore a Holter monitor for approximately one and
half hours. 15 minutes of baseline quiet breathing were recorded
before the 1 hour of meditation. The meditation protocol consisted of
a sequence of breathing and chanting exercises, performed while seated
in a cross-legged posture.  The beginning and ending of the various
meditation sub-phases were delineated with event marks.

In addition to comparing the pre-meditation and meditation states, we
also made comparisons to three healthy, non-meditating control groups
from a database of retrospective electrocardiogram (ECG) signals: (i)
A spontaneously breathing group of 11 healthy subjects (8 women and 3
men; age range 20&ndash;35, mean 29) during sleeping hours.  (ii) A healthy
group of 14 subjects (9 women and 5 men; age range 20&ndash;35, mean 25)
during supine metronomic breathing at 0.25 Hz.  (iii) A group of 9
elite triathlon athletes in their pre-race period (3 women and 6 men;
age range 21&ndash;55, mean 39) during sleeping hours.  Except for exercise
training in the triathlon athlete group, the overall general health
conditions for the meditation groups and control groups were

<h3>Signal Processing and Data Analysis</h3>

The Holter tapes were scanned and annotated using a Marquette
Electronics Model 8000T Holter scanner and annotations manually
verified.  The resulting annotation files were then transferred to a
Sparc workstation for further analysis. A small fraction (&lt; 1%) of
the instantaneous RR interval heart rate time series for each
recording was identified as outliers and deleted. Instantaneous heart
rate time series were then derived by taking the inverse of each
successive interbeat interval.</p>

We applied an ECG-derived respiration algorithm [<a 
to obtain information about the frequency and relative amplitude of
respiration.  Briefly, this technique is based on the observation that
the body surface ECG is influenced by electrode motion relative to the
heart and by changes in thoracic electrical impedance as the lungs
fill and empty. Measurement of axis shifts at each normal QRS interval
provide a continuous ECG-derived respiration signal. The relation
between this signal and respiration has been confirmed by comparing
the changes in axis direction with simultaneous measurements of chest
circumference taken with a mercury strain gauge or pneumatic
respiration transducer [<a href="#edr">5</a>].</p>
<p>We cross-correlated the heart rate time series with the ECG-derived
respiration signal.  In particular, we uniformly resampled both the
instantaneous heart rate and the ECG-derived respiration time series
at 2 Hz, then calculated the coherence [<a
 href="#Recipe">6</a>] of these two

To quantify the amplitudes of heart rate oscillations during
meditation and to compare them with those under usual basal
conditions, two independent quantitative algorithms have been applied:</p>

(1) We calculated Fourier spectral power by applying the Lomb
periodogram method for unevenly sampled data [<a
 href="#Recipe">6</a>].  Spectral
power was measured in the frequency range 0.025-0.35 Hz to ensure
that all respiration related heart rate oscillations would be

(2) We also used a Hilbert transform-based algorithm
 href="#ht">8</a>]. The advantages of using the Hilbert transform are
two-fold: (i) it does not require stationarity of the signal; and (ii)
it measures the amplitude and frequency of the dominant oscillation in
the signal at each moment. However, since the Hilbert transform can be
applied only on narrow band signals, the heart rate time series has to
be pre-processed. First, the heart rate time series signal was
bandpass filtered over the same frequency range (0.025 to 0.35 Hz)
studied in the Fourier analysis. Next, a Hilbert transformation was
performed on the filtered signal.  Thus for each subject's heart rate
time series, we obtained a sequence of amplitudes describing the
time-dependent magnitude of the oscillations.</p>

We then calculated the median value, <i>A</i><sub><i>m</i></sub>, of the oscillation
amplitude obtained by the Hilbert transform for each subject.  The
median value is a robust measurement even when a substantial number of
outliers are present in the data.  The Hilbert transform and median
amplitude procedure described here can be applied to time series with
an arbitrary number of data points.  Therefore, the results can be
compared among subjects with data sets of different lengths.</p>

<h3>Statistical Analysis</h3>

To determine the effect of meditation on oscillation amplitude and
spectral power in the meditators, values measured before and during
meditation were compared using a paired t-test.  The Student t-test
was used to compare values obtained during meditation to those
obtained from each control group. A <i>p</i> value of less than 0.01
(two-sided) was used as the level of significance for rejecting the
null hypothesis that values measured during meditation were similar to
those obtained outside of meditation.  Since the number of
subjects in each meditation group was small, we pooled these subjects
(<i>n</i>=12) for the comparisons with the control groups.  Statistical
analysis was performed using SAS software release 6.12 (Cary, North
Carolina).  Results are reported as mean 
standard deviation.</p>


Figure 1 shows representative instantaneous heart rate plots for one
Chi meditator and one Kundalini meditator.  Two features stand out: 1)
The extremely prominent heart rate oscillations for both subjects
during meditation.  Spectral analysis of these heart rate time series
confirmed a peak in the range of 0.025&ndash;0.35 Hz for both groups of
meditators. For example, Figure 2 shows illustrative data from another
Chi meditator with a spectral peak around 0.05 Hz. 2) The overall
variability of the time series. The heart rate dynamics typically
showed highly complicated fluctuations, rather than a quiescent
&ldquo;steady state.&rdquo;</p>

To test the hypothesis that these extremely large amplitude
oscillations were related to breathing, we studied the
cross-correlation between the heart rate and ECG-derived respiration
signals.  Figure 3 shows the Fourier analysis of one subject's heart rate
and ECG-derived respiration signals. The coherence measurement
verifies that these heart rate oscillations are closely related to

Table 1 shows the group averaged measurements of median heart rate
oscillation amplitude calculated using the Hilbert transform method.
During meditation, the two meditation groups both had significantly
greater amplitude of heart rate oscillations compared to their
pre-meditation baselines, and to the other control groups. However,
there was no significant difference in the heart rate oscillation
amplitude between the pre-meditation subjects and healthy controls
during spontaneous breathing.</p>

Results of Fourier analysis, shown in Table 1, are consistent with the
Hilbert derived powers. The frequency range we studied here roughly
spans the low frequency (usually 0.04-0.15 Hz) and high frequency
(usually 0.15-0.4 Hz) bands typically used in the literature.
Therefore, the Fourier powers of this study (as well as the Hilbert
powers <i>A</i><sub><i>m</i></sub><sup>2</sup>/2), can only be approximately compared to the sum of
power in low frequency and high frequency bands in most other studies.
Furthermore, the Hilbert derived power corresponds most precisely to
the actual power of the observed oscillations of interest in the
present study. Finally, we note that when comparing Hilbert and
Fourier powers, the former are consistently lower because they are
based on a single predominant frequency of interest, whereas the
latter encompass all frequency components within a given band.</p>


The major and unexpected finding in this analysis of heart rate
dynamics during these two forms of meditation in a small number of
subjects was the presence of intermittent, extremely prominent
oscillations in the 0.025&ndash;0.35 Hz band. For example, as shown in
Figure 1, the heart rate varied over a 30&ndash;35 beat/min range within 5
sec in some of the subjects.  These oscillations, observed in both Chi
and Kundalini practitioners, correlated with slow breathing. Of note,
these oscillations were significantly larger in amplitude than the
variations associated with respiratory sinus arrhythmia observed
during the pre-meditation control state, and other healthy young
adults during metronomic or nocturnal breathing, as well as in elite
triathlon athletes during sleep. Also of note was the highly complex
nature of the fluctuations for the overall time series during the
meditative states (Fig.&nbsp;1).</p>

These findings appear to contradict a conventional notion of
meditation as only a psychologically and physiologically quiescent
(&ldquo;homeostatic&rdquo;) state.  Instead, the selected healthy individuals we
studied showed marked dynamic variability in heart rate during a state
subjectively perceived as one of profound relaxation.  These findings
raise several intriguing questions, including: 1) Do some forms of
meditation involve a type of autonomic &ldquo;exercise&rdquo; mediated, at least
in part, by specialized breathing maneuvers?  2) To what extent does
the magnitude of heart rate oscillations relate to the rate and depth
of respiration? 3) Are there &ldquo;universal&rdquo; physiological mechanisms
involved in certain types of meditative states that are triggered by
apparently disparate protocols developed in different cultures?</p>

To answer the above questions, future studies should also include
other useful physiologic signals, e.g., direct measurement of
respiration and blood pressure. Further systematic, quantitative
analysis of cardiopulmonary dynamics in a large number of healthy
subjects in different age groups, as well as those with a variety of
pathologic conditions, before, during and after various meditation
regimes should broaden our understanding of an important class of
mind-body interactions.</p>


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