DOI 10.1515/bmt-2013-0139      Biomed Tech 2014; aop

Diana Piper , Karin Schiecke , Lutz Leistritz , Britta Pester , Franz Benninger , Martha Feucht ,

Mihaela Ungureanu , Rodica Strungaru and Herbert Witte*

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

Abstract: An innovative concept for synchronization

analysis between heart rate (HR) components and

rhythms in EEG envelopes is represented; it applies time-

variant analyses to heart rate variability (HRV) and EEG,

and it was tested in children with temporal lobe epilepsy

(TLE). After a removal of ocular and movement-related

artifacts, EEG band activity was computed by means

of the frequency-selective Hilbert transform providing

envelopes of frequency bands. Synchronization between

HRV and EEG envelopes was quantified by Morlet wave-

let coherence. A surrogate data approach was adapted to

test for statistical significance of time-variant coherences.

Using this processing scheme, significant coherence val-

ues between a HRV low-frequency sub-band (0.08 – 0.12

Hz) and the EEG δ envelope (1.5 – 4 Hz) occurring both in

the preictal and early postictal periods of a seizure can

be shown. Investigations were performed for all elec-

trodes at 20-s intervals and for selected electrode pairs

(T3 ÷ C3, T4 ÷ C4) in a time-variant mode. Synchronization

was more pronounced in the group of right hemispheric

TLE patients than in the left hemispheric group. Such a

group-specific augmentation of synchronization con-

firms the hypothesis of a right hemispheric lateralization

of sympathetic cardiac control of the low-frequency HRV

components.

Keywords: EEG envelope; heart rate variability; synchro-

nization; temporal lobe epilepsy; time-variant coherence.

*Corresponding author: Prof. Dr. Herbert Witte, Institute of Medical

Statistics, Computer Sciences and Documentation, Jena University

Hospital, Friedrich Schiller University Jena, 07740 Jena,

Phone: + 49 3641933982, Fax: + 49 3641933200,

E-mail: Herbert.Witte@med.uni-jena.de

Diana Piper: Department of Applied Electronics and Information

Engineering, Politehnica University of Bucharest, Romania ; and

Institute of Medical Statistics, Computer Sciences and

Documentation, Jena University Hospital, Friedrich Schiller

University Jena, Germany

Karin Schiecke, Lutz Leistritz and Britta Pester: Institute of Medical

Statistics, Computer Sciences and Documentation, Jena University

Hospital, Friedrich Schiller University Jena, Germany

Franz Benninger and Martha Feucht: Epilepsy Monitoring Unit,

Department of Child and Adolescent Neuropsychiatry, University

Hospital Vienna, Austria

Mihaela Ungureanu and Rodica Strungaru: Department of Applied

Electronics and Information Engineering, Politehnica University of

Bucharest, Romania

Introduction It is well-known that epileptic seizure activity influences the

autonomic nervous system (ANS) in different ways. Accord-

ingly, long-term (chronic) as well as short-term (acute)

alterations of the ANS before, during, and after the seizure

have been studied [ 34 ]. Changes in heart rate (HR) and HR

variability (HRV) are the measures used most frequently to

investigate both long-term and short-term alterations of the

ANS in response to the type of epilepsy and to the evolution

of the epileptic seizure [ 12 , 20 ]. HRV can be considered as

a mirror of neuronal influences on the cardiac pacemaker

and as one of the important functions of the ANS [ 13 ]. It has

been demonstrated by several studies that preictal HRV

patterns alone can be beneficially used for seizure onset

prediction (e.g., [ 16 ]). Results from basic research suggest

that the dynamics of the HRV reactions, which are depend-

ent on specific characteristics of the seizure, may provide

more information on the organization of the ANS [ 13 ] and

the mechanisms supporting ANS changes.

This methodological study aims at the detection of syn-

chronizations between HRV components and EEG activity

before, during, and after a seizure in refractory temporal

lobe epilepsy (TLE) patients (children and adolescents), in

order to reveal functional relationships between the ANS

and cortical processes. Time-variant EEG band activity is

usually quantified by analyzing the envelope of the band-

pass filtered EEG. The question arises: why might synchro-

nization between HRV components and EEG envelopes be

assumed? An impetus was provided by one of our previ-

ous studies, which investigated the time-evolution of HRV

components in TLE patients before, during, and after the

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2      D. Piper et al.: Synchronization between heart rate variability and EEG

seizure [ 32 ]. We demonstrated that a pronounced phase-

locking between the low-frequency (HRV-LF) and the

high-frequency (HRV-HF) component occurs in the pre-

ictal period. The HRV-LF component is closely associated

with the Traube-Hering-Mayer waves ( ≈ 0.1 Hz; “ Mayer

waves ” ) of the systemic arterial blood pressure (ABP),

and the HRV-HF component is known as respiratory sinus

arrhythmia. Such an increased phase locking indicates

that this component can be approximately described as an

oscillation with a stable phase, in particular, 2 min before

seizure onset and in the early postictal period (recupera-

tion period). The increased phase locking is accompanied

by a higher degree of predictability (value of the largest

Lyapunov exponent decreases) and quadratic phase

coupling between both components (bicoherence values

increases). In a preliminary study regarding time-vari-

ant changes of the HRV components and EEG frequency

bands, we found time epochs in which the EEG envelopes

were characterized by a 0.1-Hz rhythmicity (e.g., of the

δ and the α band [ 29 ]). Additionally, other studies have

shown that such synchronizations between HRV-LF and

the EEG frequency band activity exist (e.g., [ 28 ]).

The following findings shaped our methodological

approach and the analysis strategy:

1. Saleh et al. [ 31 ] argued that the time range before sei-

zure onset is most favorable to investigate the mecha-

nisms supporting ANS changes. This is because the

spread of epileptic activity or seizure-related heart

and circulatory alterations have not yet taken place.

Therefore, the focus of our investigations is on preic-

tal data analysis.

2. The epileptic focus of mesial temporal lobe epilepsy

(mTLE) seizures is located in the limbic structures

( “ limbic seizure ” ), which are involved in the regula-

tion of the ANS. The mTLE is the most common form of

epilepsy, where the associated pathological substrate

is usually hippocampal sclerosis [ 10 ]. The mTLE also

appears to be one of the most medically refractory

forms of human epilepsy [ 10 ]. Leutmezer et  al. [ 19 ]

showed that the HR increases, which occur during the

ictal period, are more pronounced in patients with

mTLE compared with other TLE and epilepsy types.

Therefore it would be interesting to investigate syn-

chronization effects with a particular focus on mTLE.

3. It has also been shown that the HR is significantly

increased in the preictal period (preictal tachycardia),

when the focus of the TLE is in the right hemisphere

[ 31 ]. In contrast, no statistically significant changes

could be observed in a left-focus group. The authors

stated that a right hemispheric lateralization of the

sympathetic cardiac control can be assumed. These

results were confirmed for children with TLE. Mayer

et al. [ 21 ] detected “ significant differences in HR evolu-

tion depending on location and side of seizure onset ” .

They found that an early and high HR increase was

primarily associated with right hemispheric mTLEs.

Consequently, our strategy comprises a comparative

analysis of two groups; one with left hemispheric

mTLE ( “ left-focus group ” ) and the other with right

hemispheric mTLE ( “ right-focus group ” ).

4. Time-frequency techniques are most appropriate to

analyze acute changes in HRV and EEG [ 39 ]. Conse-

quently, relationships between HRV and EEG should

also be investigated by time-variant and frequency-

selective approaches. The expected synchronization

effect must be seen as a physiological epiphenom-

enon because HRV-generating and EEG-generating

structures cannot interact with each other in a causal

relationship. Therefore, we used time-variant coher-

ence, as coherence is an established correlative

measure for the detection and quantification of syn-

chronization effects [ 40 ].

5. As already mentioned, results from one of our recent

studies [ 32 ] showed that premonitory information

on imminent seizure onset can be derived from HRV-

LF, which is associated with the Mayer waves in ABP.

Therefore, we focused our study on HRV-LF. The EEG

of the interictal and preictal periods in TLE children is

typically characterized by temporal spike or sharp-wave

discharges and temporal intermittent rhythmic δ activ-

ity [ 24 ]. Consequently, the focus is on EEG δ activity.

Our methodological study provides a new analysis strat-

egy with the possibility of expanded application, taking

into account all such previous findings from HRV and EEG

analysis in TLE patients.

Subjects and methods

Subjects

The data were recorded during presurgical evaluation of

the patients at the Vienna pediatric epilepsy center fol-

lowing a standard protocol as described by Mayer et  al.

[ 21 ]. From the group of 20 patients, only those were

selected who had at least one seizure with a record-

ing time of 10 min (at least 5 min before and 5 min after

seizure onset). Seizure onset and termination in the EEG

was determined independently by two experienced neu-

rologists. Four children were added to the patient group

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D. Piper et al.: Synchronization between heart rate variability and EEG      3

to achieve comparable numbers of male/female patients

and right/left focused seizures as in [ 21 ]. The resulting

group of 18 children (median age 9 years 4 months, range

6  years 6  months to 18  years 0 months; median seizure

length 88 s, range 52 – 177 s) was subdivided into a group

with left (n = 9) and a group with right hemispheric TLE

(n = 9) (called left-focus and right-focus group). Patients ’

demographic data and relevant information about the sei-

zures of both subgroups are given in Table 1 .

Data acquisition and preprocessing

The extended 10 – 20 systems with additional temporal

electrodes for the EEG (23 channels) and one channel for

the ECG recording were used. All signals were recorded

against the reference electrode CPz and filtered (1 – 70

Hz), before they were digitized by an analog-digital con-

verter (sampling frequency 256 Hz, 12 bit) for further data

analysis. A commercially available video-EEG monitoring

system was used for data acquisition and off-line data

processing (Phoenix EEG Monitoring System; EMS Co.,

Korneuburg, Austria).

The first preprocessing step for the EEG was a down-

sampling to 64 Hz, using a low-pass IIR filter, Chebyshev

type 1 order eight, that is applied in forward and reverse

directions to eliminate phase delay. The filtering was fol-

lowed by an artifact removal procedure, using independ-

ent component analysis (ICA) provided by the Field Trip

toolbox [ 25 ]. Thereafter, a referencing of the EEG to an

average reference montage was performed.

QRS detection was performed after digital band-

pass filtering (10 – 50 Hz) of the ECG and interpolation

by cubic splines (interpolated sampling frequency 1024

Hz) to detect the time of the maximum amplitude of each

R-wave, and the resulting series of events was used for the

HR computation, i.e., this series of events was low-pass

filtered by means of a FFT-filter (cutoff frequency  ≤  half of

the mean HR). The procedure is known as French-Holden

algorithm [ 11 ], which leads to the low-pass filtered event

series (LPFES), a standard HRV representation in the

time domain. Theoretically, the LPFES is the result of an

exact demodulation of the pulse-frequency modulated

series of events (QRS) [ 6 ]. In contrast, the instantane-

ous heart rate (IHR) representation is an approximation

of the demodulation. The superiority of the LPFES (vs.

IHR) for the investigation of rhythmic HRV components

was shown by Milde et al. [ 23 ]. The final HRV representa-

tion was obtained via multiplication of the LPFES with

the sampling rate and with 60 beats per minute (bpm)

and downsampled to 8 Hz. An artifact rejection was per-

formed manually to minimize the influence of false QRS

triggering.

Methods

Frequency-selective Hilbert transform: The frequency-

selective Hilbert transform of a signal x ( t ) can be calcu-

lated with the help of the Fourier transform [ 39 ]:

1[ ]( ) [ ( ) ( ) [ ]( )]( )H x t F i sign f BP f F x f t−= − ⋅ ⋅ ⋅

(1)

Table 1   Patients ’ demographic data and relevant information on the seizures.

Left-focus group   

Right-focus group

Pat ID   Age (y/m)

  Gender (m/f)

  Local. (M/L)

  Duration (in [s])

Pat ID

  Age (y/m)

  Gender (m/f)

  Local (M/L)

  Duration (in [s])

3   6/11   m   M   74   2   8/7   f   M   155

8   17/7   m   L   52   5   12/1   m   M   87

9   9/4   f   M   89   6   13/4   f   M   72

11   10/0   f   M   74   7   12/4   f   L   90

16   6/6   m   M   72   12   8/7   f   M   58

18   9/5   f   L   177   13   7/8   f   M   94

20   8/2   f   M   70   15   9/4   f   M   80

21   9/5   m   M   111   23   6/10   m   M   119

22   11/7   m   M   100   24   18/0   f   M   110

n = 9   9/5   Median   74   n = 9   9/4   Median   90

  6/6   Min   52     6/10   Min   58

  17/7   Max   177     18/0   Max   155

Relevant information divided according to left or right hemispheric seizure (18 children, n = 9 in each subgroup of patients). ID of patient, age

(in years/months), gender (m, male; f, female), localization (M, mesial; L, Lateral) and duration of seizure (in [s]) are given for each patient.

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4      D. Piper et al.: Synchronization between heart rate variability and EEG

where F – 1 is the inverse of the Fourier transform, i is the

imaginary unit, sign ( f) is the signum function

1 for 0

( ) 0 for 0

1 for 0

f

sign f f

f

⎧− <⎪= =⎨⎪ >⎩

(2)

and BP ( f ) is a band-pass operator by which a frequency

band in the frequency domain was selected before the

inverse FFT was carried out. The Hilbert transform of x ( t )

is the imaginary part of a band-related analytic signal

( )a

HT BPx t with

( ) ( ) [ ]( )a

HT BP BPx t x t i H x t= + ⋅

(3)

where x ( t) BP

is the corresponding real part, which can be

computed by an identical band-pass filtering (by using

BP ( f ), FFT filter) of the signal x ( t ). The frequency bands

1.5 – 4 Hz (sub- δ ), 4 – 8 Hz ( θ ), 8 – 13 Hz ( α ), and 13 – 18 Hz ( β )

were selected by using the frequency-selective HT imple-

mentation described by Witte et al. [ 41 ]. The envelope of a

band-pass filtered signal is described by

2 2( ) () [ ] )( .

BPenv t x t H x t= +

(4)

Morlet wavelet transform: The wavelet transform uses

a base waveform (window) called mother wavelet from

which all other filters are obtained by scaling it. The pre-

ferred method for the time-scale (multifrequency) analy-

sis is the continuous Morlet wavelet transform (CMWT).

The CMWT can be formulated as in [ 38 ]. The mother

wavelet of the CMWT is the complex-valued function

2 2

0

04

1( ) exp( ) exp   exp .

2 2

tt i t

ωψ ω

π

⎛ ⎞⎛ ⎞ ⎛ ⎞= − − −⎜ ⎟⎜ ⎟ ⎜ ⎟⎝ ⎠⎝ ⎠⎝ ⎠

(5)

Note that the DC correction can be omitted for reason-

ably large ω 0 . The complex analytic signal for the scale s

can be obtained by linear convolution

*

 

1( , ) ( ) .a

CMWT

tx t s x d

ss

ττ ψ τ

∞

−∞

⎛ ⎞−= ⎜ ⎟⎝ ⎠∫

(6)

Here, * denotes the complex conjugate. Owing to the

application in EEG analysis, a frequency-based notation is

preferable. We obtain with 0 2

fs

ω

π=

*

00

2( , ) ( ) .

2

a

CMWT

f tx t f x d

f

π ττ ψ τ

ωω

π

∞

−∞

⎛ ⎞⎜ ⎟−⎜ ⎟=⎜ ⎟⎜ ⎟⎝ ⎠

∫

(7)

We adapted the tuning parameter ω 0 = 2 π to match the

short-time characteristics of the signal, which ensures a

minimization of artifacts due to inappropriate time-fre-

quency resolution (signal-adapted CMWT).

The time-variant power spectrum was computed by

2( , ) | ( , ) | .a

CMWTS t f x t f=

(8)

The time-frequency resolution of the CMWT is fre-

quency-dependent. Higher frequencies lead to a better

time resolution (TR) but also to worse frequency resolu-

tion (FR). The time-variant power spectra and coherences

were calculated at first for the frequency interval 0 – 0.5 Hz.

The frequency range 0.08 – 0.12 Hz was chosen for further

computations of the time-variant coherence. We used

the standard deviation of the Gauss envelope in the time

domain and the standard deviation of the Gauss curve in

the frequency domain as measure for the TR and FR [ 39 ].

The following TRs and FRs result for the frequencies we

are most interested in: at 0.3 Hz (TR = 5 s, FR = 0.2 Hz) and

at 0.1 Hz (TR = 15 s, FR = 0.06 Hz).

Coherence: For the time-variant spectrum and coher-

ence computation, the CMWT was applied. Coherence is

calculated with the help of the time-variant CWMT cross

spectrum S HRV

/ envelope

( t, f ) between HRV and the EEG enve-

lope as well as of their time-variant spectra S HRV

( t, f ) and

S envelope

( t, f ):

2

/

/

| ( , ) || |( , )

( , ) ( , )

HRV envelope

HRV envelope

HRV envelope

S t fC t f

S t f S t f=

⋅

(9)

with

*

/, ( ) ( , ) ( , )a a

HRV envelope CMWT HRV CMWT envelopeS t f x t f x t f= ⋅

(10)

by using the CWMT-related analytic signals according to

equation 7 (the superscript * is the complex conjugate).

In order to compute the time-variant coherence,

the envelopes were downsampled to 8 Hz to match the

HRV ’ s sampling frequency, and a time smoothing of the

cross-spectrum and of the two spectra was carried out

to obtain an appropriate estimation. We used rectangu-

lar time windows with a length of 128 (16 s), 256 (32 s),

and 512 (64 s) time points. The 128-point window pro-

vides a TR for HRV-LF, which is similar to that provided

by CMWT. The best compromise between a sufficient

smoothing (estimation properties) and a satisfactory TR

was achieved by using the window with 512 points. The

FR remains ( ≈ 0.06 Hz for 0.1 Hz, see above). The mean

representing the frequency band 0.08 – 0.12  Hz was cal-

culated for each time point of the time-variant coherence

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D. Piper et al.: Synchronization between heart rate variability and EEG      5

to obtain the time course of the band coherence. For the

topographic analysis, the mean value of the time-vari-

ant coherence in a 20-s analysis interval was computed

(mean over time).

Statistics: The statistical hypothesis testing for coher-

ence analysis was performed by using surrogate data. The

null hypothesis is that there is no coupling (synchroniza-

tion) between EEG envelopes and HRV. The surrogate data

were obtained by destroying the phase information for

both signals by means of phase randomization [ 36 ]. This

was carried out for 1000 repetitions, and the 95th (90th)

percentile of the surrogate time-variant coherence was

computed. The mean over time of the 95th (90th) percen-

tile was set as the “ 5% (10%) threshold ” for statistically

significant coherence values. For the topographic analy-

sis, the mean value of the time-variant coherence in a 20-s

analysis interval was compared with the 5% threshold to

obtain the significant values for each electrode.

Processing concept

The processing scheme used is represented in Figure

1 . The first step of the EEG processing was the artifact

removal by means of an ICA approach in order to reduce

the influence of ocular and movement artifacts [ 18 ].

Thereafter, a re-referencing of the recordings to an average

reference montage was performed. For analysis 20 chan-

nels (Figure 2) were used. Subsequently, the envelopes of

the frequency bands 1.5 – 4 Hz (sub- δ ), 4 – 8 Hz ( θ ), 8 – 13 Hz

( α ), and 13 – 18  Hz ( β ) were computed by means of the

frequency-selective Hilbert transform (equations 1 – 4).

Each of these envelopes was used as one input signal for

time-variant coherence analysis (equation 9). The HRV

(LPFES representation) serves as the second input. The

δ sub-band 1.5 – 4  Hz in combination with the restricted

HRV-LF band (0.08 – 0.12 Hz) showed the best results and

was chosen for this study. In Figure 2 , examples of both EEG and HRV signals

are shown for the whole analysis interval (600 s). Twenty

EEG channels were used for the analysis (montage,

Figure 2 right side). The EEG signal (A) of the channel T3

of one patient (ID 9) is shown. At 300 s, the seizure onset

is localized. The average reference signal is depicted as

overlay (gray) in Figure 2 A and separately in Figure 2 B.

The average reference signal contains signal compo-

nents, which simultaneously occur in all signals. In

Figure 2 C, the δ band activity (1.5 – 4 Hz) and in Figure 2 D,

the corresponding envelope are depicted. The HRV is

represented below ( Figure 2 E). Both the envelope and

the HRV rise immediately after seizure onset (300 s). The

seizure lasts approximately 60 s (the median duration for

all patients in both groups is 88 s). Approximately 50 s

after onset, the HRV decreases toward the preictal mean

HRV value. This is a typical ictal tachycardia, which can

be observed in all patients of both groups. The postictal δ

activity level remains higher than the level in the preictal

period.

Figure 1   Processing scheme used for the synchronization analysis between HRV and EEG envelopes.

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6      D. Piper et al.: Synchronization between heart rate variability and EEG

Results

Time-variant HRV analysis

First, the HRV data of both groups were investigated by

means of time-frequency spectral analysis (CMWT, equa-

tions 5 – 8). This initial analysis step aimed at the identi-

fication of HRV patterns, in particular, the timing and

grouping of HRV-related Mayer waves in connection with

corresponding characteristics of the respiratory sinus

arrhythmia (HRV-HF). In a recent HRV study [ 32 ] (in

which the patients were not divided according to left- or

right-sided seizure) a clear separation of the Mayer wave-

related HRV-LF (around 0.1 Hz) and the HRV-HF range

(between 0.25 and 0.4 Hz) before seizure onset (300 s) was

shown. The results of the time-variant HRV power spec-

trum analysis including all patients (n = 18) is represented

in Figure 3 A. The HRV-HF exists until and collapses with

the onset of the seizure (when normal breathing changes

[ 14 ]). Approximately 90 s after seizure onset, the HF range

recur with strong power disturbances (390 – 480 s) and

becomes less pronounced at the end of the analysis inter-

val (500 – 600 s). Transient clusters of HRV-LF component

can be observed in particular during the preictal period.

For this study, the grand mean analysis for both subgroups

( Figure 3 B right-focus group, Figure 3 C left-focus group)

was repeated. It can be demonstrated that all the findings

Figure 2   Examples for recorded and processed signals. (A) The EEG at electrode T3 with the overlay (gray) of average reference activ-

ity, (B) average reference activity, (C) activity of the δ sub-band 1.5 – 4 Hz, (D) envelope of the δ sub-band and (E) HRV (linear trend was

subtracted), all given for one child (ID 9). Additionally, overview of recorded EEG at all electrode positions is depicted for the same

child.

Figure 3   Results of the time-variant HRV power spectrum

analysis. (A) Grand mean over all 18 children, (B) mean of the

right-focus group, and (C) mean of the left-focus group are dis-

played. The white horizontal rectangular frame designates the

LF range (0.08 – 0.12 Hz), and the white elliptic frames indicate

clusters of HRV-related Mayer waves. Time-frequency representa-

tions of power spectrum are given [color bar in (bpm 2 )). The

marks 1 – 4 designate time intervals shown in Figure 6 in which

significant coherence (HRV-LF vs. δ envelope) ranges occur.

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D. Piper et al.: Synchronization between heart rate variability and EEG      7

given above can be confirmed for the right-focus group.

Moreover, the effects in the right-focus group are more

enhanced ( Figure 3 B), but almost vanish in the left-focus

group ( Figure 3 C). In Figure 3 , the time-frequency ranges

are indicated by white elliptic frames for which time inter-

vals with significant coherences between HRV-LF and δ

envelope ( Figure 6 ) exist.

Results of the topographic analysis

A time-variant topographic analysis was performed

in order to obtain a rough overview of the time evolu-

tion, variability (stability), and laterality of topographic

synchronization patterns. The mean coherence values

between the HRV-LF and δ envelope were calculated for

disjunct 20-s intervals for each electrode (mean over time

and frequency). The mean coherence values are repre-

sented at the location of the corresponding electrode,

thus, a coherence map sequence for each patient results.

The group-related coherence map sequences result from

an averaging of the patient-related sequences (i.e., mean

over patients), which are represented in Figure 4 . In addition, particular electrodes that can optimally

describe these pattern characteristics were identified. For

these electrodes, a more detailed analysis (higher TR) was

carried out (Section Results for selected electrodes).

It can be shown that the topographic coherence pat-

terns change “ continuously ” , i.e., the transition from

one to another pattern does not occur abruptly. Smear-

ing effects caused by the necessary time smoothing of the

single-trial time-variant coherence estimation contribute

to the pattern evolution. However, the achieved TR is sat-

isfactory and appropriate to the occurrence of HRV-related

Mayer wave clusters, which were analyzed with a fourfold

higher TR.

The synchronization patterns are most pronounced

1 min before seizure onset (interval 240 – 260 s). After onset,

the topographic distributions tend toward the respective

focus hemisphere, in particular, in the left-focus group.

The right-focus group ( Figure 4 B) shows patterns with

higher coherence values than the left-focus group ( Figure

4 A). This is particularly true for the preictal period. For

the left-focus group, a stable pattern evolves after seizure

onset in the left hemisphere. Starting from electrode C3,

the pattern involves P3 and the neighboring central elec-

trodes. It must be noted that C3 also shows high coherence

values in the preictal period (maximum between 200 and

220 s), which decrease toward seizure onset.

Each epileptic seizure is an extremely individual event

(severity, focus localization, activity spreading, etc.).

Therefore, on the one hand, the continuous evolution

of the averaged topographic patterns indicates uniform

(systematic) effects; on the other hand, blurring effects

caused by individual variations cannot be excluded. It

should be noted that our groups do not include only TLE

patients with a mesial focus (Table 1, left-focus group:

two patients with a lateral focus; right-focus group: one

patient with a lateral focus). Therefore, two representative

analysis results derived from one patient of each group

should demonstrate that our processing concept enables

an individual analysis. The results for each patient are

depicted in Figure 5 . These cases clearly show that (1)

topographically extended areas of significant coherence

occur 1 min before seizure onset in the patient with a right-

hemispheric mTLE, (2) immediately after seizure onset,

no electrode shows any significant coherence values, and

(3) a lateralization of locally circumscribed patterns (C3

Figure 4   Results of group-related mean coherence (HRV-LF vs. δ envelope) topography for subsequent 20-s intervals before and shortly

after the seizure onset (300 s, red arrow). (A) The left-focus group (rectangular white frame = T3 ÷ C3) and (B) the right-focus group (rectangu-

lar white frame = T4 ÷ C4) are designated.

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8      D. Piper et al.: Synchronization between heart rate variability and EEG

for the patient with the left-hemispheric and T4 for the

patient with the right-hemispheric TLE) can be obtained

at the end of the seizure and in the early postictal period

( Figure 6 , designations ). The electrode pairs T3 ÷ C3

and T4 ÷ C4 have been identified to describe the patterns ’

characteristics best (temporal and topographic dynam-

ics). In addition, it can be expected that T3 and T4 pick

up the neuronal activity from the temporal lobe. The elec-

trodes are designated by a frame ( Figures 4 and 5).

Results for selected electrodes

In Figure 6 , the analysis results of both groups for the

electrodes T3 and T4 are represented. For the right-focus

group, the electrode T4 is on the focus side and T3 on the

opposite side and, for the left-focus group, vice versa.

In the right-focus group, approximately 100 s before the

seizure onset, an increase of the coherence course at

T4 occurs, which exceeds the 5% threshold for approxi-

mately 30  s (designation in Figure 6 , covers the time

segment 220 – 260 s). This duration agrees with the dura-

tion of a HRV-related Mayer wave cluster – approximately

three Mayer waves – which can be seen in the time-vari-

ant HRV spectrum (designation in Figure 3 B). In addi-

tion, the 10% threshold is exceeded in a time interval

before (140 – 170 s), i.e., between 140 s and 260 s, a strong

coupling between the HRV-LF range and the δ envelope

exists for the group data (n = 9). At the opposite electrode

(T3), such an increase in the preictal period can also be

observed, but the 5% threshold is only exceeded for some

seconds. The corresponding points at which the thresh-

olds exceeded the 10% threshold ( > 10 s, i.e., one period of

a Mayer wave) are in the time range between 170 and 190

s. Additionally, a strong coupling between HRV-LF and the

δ envelope can be observed in the postictal period at T3

(designation in Figure 6 ) at a time interval around 450 s.

In this period, the HRV-LF amplitude is high (designation

in Figure 3 B).

For the left-focus group, only a short over-crossing

of the threshold in the preictal period can be observed

(between 130 and 135 s). This result agrees with a reduced

number of HRV-related Mayer waves ( Figure 3 C) in com-

parison with the preictal period of the right-focus group

( Figure 3 B). It was interesting to note that the left-focus

group substantially exceeded the 5% threshold in the pos-

tictal period at both electrode sites (designations and ).

High coherence values occur approximately 200 s after the

seizure onset, and this event lasts approximately 100 s.

These significant coherence values correlate to the occur-

rence of LF activity in the HRV (augmented occurrence of

HRV-related Mayer waves in Figure 3 C, designation ).

Figure 5   Results of coherence topography for subsequent 20-s intervals for two representative patients, one for each group. (A) Topo-

graphic coherence maps between HRV-LF and δ envelope and (B) significant coherence at electrodes (red designation) are given for one

left-focus group member (ID 9, rectangular white frame = T3 ÷ C3) and one right-focus group member (ID 2, rectangular white frame = T4 ÷ C4).

The time of the seizure onset is designated by a red arrow.

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D. Piper et al.: Synchronization between heart rate variability and EEG      9

As mentioned above, such a postictal coherence “ peak ”

(maximum 150 s after the onset, designation in Figure 6 )

can also be detected in the right-focus group, but only at

the electrode on the non-focus side. HRV-related Mayer

waves occur in the time range of this “ peak ” ( Figure 3 B).

At the electrode C3, the highest coherence values (not

illustrated) of all electrodes can be observed in the left-

focus group (C3 is at the focus side), where “ peaks ” before

(maximum around 250 s) and immediately after the onset

(between 350 and 400 s) exists. The minimum between

both peaks is located at the seizure onset. Around the

peaks, HRV-LF activity can also be observed, which is

long-lasting before the onset.

Summary of the results

The results of the coherence analysis can be summarized

as follows:

– During the preictal and the postictal period, longer

epochs exist, which are designated by significant

coherence values between HRV-LF and the δ envelope.

The HRV-related Mayer waves are pronounced during

these epochs, i.e., the HRV-LF band shows clusters of

high-amplitude events (waves).

– The topographic analysis shows high coherence val-

ues at C3, T3 and C4, T4 in particular before and after

the seizure. The coherence at T3 and T4 decreases

during the seizure in both groups. The HRV analysis

shows that during the seizure, the LF band is char-

acterized by small amplitude values, and the HRV ’ s

rhythmicity changes toward lower frequencies.

– The averaged coherence curves as well as the topo-

graphic analysis of the preictal period show that the

group with the right-hemispheric TLE is characterized

by higher coherence values. The presence of HRV-

related Mayer waves (HRV-LF) is also massively pro-

nounced for the right-focus group.

Figure 6   Results of a group-related time-variant coherence analysis (HRV-LF vs. δ envelope) for the electrode sites T3 and T4 (abscissa

in [s]). The dashed vertical line designates the seizure onset at 300 s. The red horizontal line shows the 5% threshold for the detection

significant coherence values, and the green horizontal line shows the 10% threshold, accordingly. The gray rectangular frames represent

the time ranges in which coherence exceeds the 10% threshold. The marks 1 – 4 designate time ranges in which coherence exceeds the 5%

threshold longer than 10 s.

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10      D. Piper et al.: Synchronization between heart rate variability and EEG

Discussion

Comparison with the results of other HRV-EEG coupling studies

It is shown that our methodological approach allows the

detection of synchronizations between HRV-LF rhythms

and rhythmical EEG activity changes. To our knowledge,

there has been no study, thus far, which has investigated

the synchronization or the correlative coupling between

HRV components and EEG activity in epileptic patients.

The findings that preictal and postictal EEG δ activity is

rhythmically modulated and that the modulation rhythm

is correlated with the HRV-related Mayer waves (HRV-LF)

are new. The time-variant coherence analysis is a linear

time-frequency technique, which detects transient cor-

relative relationships between signals (e.g., synchroniza-

tions). The coherence is amplitude independent; however,

its values depend on the signal-to-noise-ratio (SNR) of

both signals (low SNR causes low coherence) [ 4 ], i.e., for

HRV and EEG envelopes. Therefore, a statistically defined

threshold detecting significant coherence values is inevi-

table. It can be safely assumed that significant coherence

values indicate synchronization between HVR and EEG

activity (envelope).

There are only a few studies investigating the cou-

pling between HRV-LF and EEG activity modulations.

During the quiet sleep of preterm neonates, the EEG

alternates between a high-voltage burst discharge and

a suppressed interburst activity ( “ tracé discontinue ”

EEG pattern), where a HR acceleration is coupled to the

burst onset. We found that the higher the burst ampli-

tude ( > 60 μ V), the more pronounced is the HR change

[ 33 ]. It should be noted here that preterm neonates have

a deficit in ANS activity and a sympathetic-parasympa-

thetic imbalance characterized by sympathetic predomi-

nance [ 17 ]. The synchronous changes of EEG and HR we

have discussed are an indication for a coupling between

cortical, thalamocortical, and central autonomic brain

areas. Such a coupling between HR and EEG during the

burst phases in anesthetized patients (burst-suppression

patterns) has also been found [ 43 ]. Pfurtscheller et  al.

[ 28 ] recently demonstrated a coupling over alternating

epochs (duration approximately 100 s) between prefron-

tal oxyhemoglobin rhythms (0.07 – 0.13 Hz) and central

EEG α and/or β envelopes in the resting brain. In two

subjects, they found that oxyhemoglobin and EEG enve-

lopes ( β band) were approximately in-phase with ABP

oscillations with an extremely high coupling between

ABP and oxyhemoglobin rhythms. Roche-Labarbe et al.

[ 30 ] demonstrated that EEG bursts (quiet sleep period) in

preterm neonates are accompanied by a transient stereo-

typed hemodynamic response involving a decrease in the

oxyhemoglobin concentration followed by an increase.

In sleep research, further studies exist, which show a

temporal correlation between frequency band activity

and HRV, e.g., during paradoxical sleep (between HRV

and δ - θ bands [27]). Jurysta et al. [ 15 ] demonstrated that

a closed connection between cardiac autonomic activ-

ity and spectral EEG bands exists. The δ band shows the

highest variations in response to HRV-HF variability, and

ANS activity precedes changes in the EEG during sleep

in healthy young men. These results from the literature

demonstrate that a correlation between HRV characteris-

tics and EEG activity may occur in extreme physiological

situations.

Physiological mechanisms

Mayer waves in systemic ABP are strongly correlated

with the oscillations of efferent sympathetic nervous

activity, and the baroreflex plays a major role in the

generation of Mayer waves. In contrast, the Mayer wave-

associated HRV-LF component includes most probably

both sympathetic and parasympathetic (vagal) influ-

ences [ 5 ]. A strong correlation between HR and pressure

variations in the 0.1-Hz frequency range was shown [ 7 ].

In TLE patients, the baroreflex function is chronically

impaired, e.g., the LF transfer function gain between

ABP and HRV, which determines the baroreflex function

[ 3 , 9 ], is reduced. Other studies have shown that TLE

patients are characterized by a dysfunction of the car-

diovascular autonomic regulation (autonomic instabil-

ity [ 13 ]), manifested as impaired HR responses to certain

stimuli [ 1 ]. Acute HRV changes in TLE patients occur due

to a chronic dysfunction in cardiovascular autonomic

regulation, i.e., this dysfunction might enhance change

in the organization of the Mayer waves in the preictal

period. The cause of such a HRV-LF augmentation and,

in particular, of the therewith associated synchroniza-

tion between HRV-LF and the δ envelope must be asso-

ciated with acute neuronal and non-neuronal brain

processes, which evolve some minutes before the onset

of the seizure and which cannot be detected by scalp

EEG. It was recently shown that focal hemodynamic

changes (cerebral blood flow (CBF) increases, and

hemoglobin oxygenation decreases) precede seizure

onset (humans and animals) by approximately 20 s [ 26 ,

44 ]. These changes can be measured (optical imaging)

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D. Piper et al.: Synchronization between heart rate variability and EEG      11

at the focus of the seizure. The etiology of the increase of

CBF before seizure onset is unknown [ 26 ]. The authors

hypothesize that these preictal CBF changes could be

elicited by subtle neuronal or glial events, astrocyte- or

pericyte-medicated signaling or local potassium, and

local neurotransmitter/neuropeptide release. If such

preseizure processes could influence the autonomic

centers within the central nervous system, synchroniza-

tion or resonance phenomena could result. CBF and its

sympathetic regulation might play an important role. It

is known that Mayer waves in systemic ABP create vari-

ations in CBF velocity in the intracranial arteries of the

same frequency [ 35 ]. This establishes a link between

ABP, HRV-LF, and CBF, which is connected with neuronal

activity via neurovascular coupling [22]. Neurovascular

coupling describes the relationship among neuronal

activity, metabolism, tissue oxygenation, and CBF.

The influence of the focus side

Jansen and Lagae [ 13 ] noted that “ due to the hemispheric

specific organization of the central autonomic nervous

system, autonomic symptoms in epileptic seizures can

provide lateralizing and localizing information ” . Our

results for the right-focus group confirm that the syn-

chronization effects between HRV-LF and the δ envelope

depends on the lateralization of the seizure.

Additionally, we have found synchronizations

between the HRV-LF and δ envelope in the early pos-

tictal period, i.e., immediately after the end of the epi-

leptic seizure. This postictal synchronization was more

pronounced in the left-focus group. Severe postictal dis-

turbances (dysregulations) of the ANS over a time range

of 5 – 6 h are described by Toth et al. [ 37 ] (HRV analysis).

Therefore, it is not surprising that we found stronger

synchronizations in both groups in comparison to

those of the preictal period. The mechanisms discussed

above might also contribute to postictal synchronization

effects.

Outlook

Several studies have suggested that the left hemisphere

modulates the parasympathetic (vagal) tone. Accordingly,

it would be interesting to investigate synchronization

between EEG frequency band activity and HRV-HF (respira-

tory sinus arrhythmia). It would also be of interest to incor-

porate ABP and respiratory movements into the analysis.

Subsequent analyses should include a cogent focus on the

interictal period to investigate the “ spontaneous ” long-

term organization of the HRV-related Mayer waves as well

as their synchronization to EEG activity. However, such

investigations require long-term recordings and monitor-

ing of cardiovascular-cardiorespiratory parameters and

the EEG. Our processing concept can be adapted to such

requirements, e.g., the interval-based HT can be replaced

by narrow-band Hilbert filters [ 2 ] and a filter bank-based

CWMT implementation can be used for coherence compu-

tation. The threshold can be determined, for example, by

a supervised classifier on the basis of representative train-

ing data [ 8 ]. Recordings from subdural and depth EEG elec-

trodes in order to capture local cortical activity can be used.

Most methods to determine seizure prediction use intracra-

nial EEG recordings due to their higher fidelity in compari-

son to scalp EEG [ 42 ]. The reliability of our results, under

conditions as described above and with a broadening of

the methodological scope, will still require more intensive

basic research before possible utilization in clinical settings

to aid in the prediction of seizures. Importantly, our results

confirm those of other studies and provide a deeper insight

into the time-variant organization of interactions between

the ANS and cortical processes.

Acknowledgment: This work was supported by the DFG

under Wi 1166/12-1 and by the Romanian Ministry of

Labour, Family and Social Protection through the Finan-

cial Agreement POSDRU/107/1.5/S/76903 (D. Piper).

Received December 17, 2013; accepted February 28, 2014

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