HEART RATE VARIABILITY ANALYSIS OF CHILDREN WITH

REFRACTORY EPILEPSY BEFORE AND AFTER THE VAGUS

NERVE STIMULATION

Milica Milošević, Steven Vandeput, Sabine Van Huffel

Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven

Kasteelpark Arenberg 10 postbus 2446, B-3001 Leuven, Belgium

Katrien Jansen, Lieven Lagae

Department of Pediatrics, University Hospital Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium

Keywords: Refractory epilepsy, Heart rate variability, Nonlinear HRV parameters, Vagus nerve stimulation.

Abstract: Vagus nerve stimulation (VNS) is a well-known therapeutic option for patients with refractory epilepsy who

do not respond to adequate anti-epileptic drugs. Heart rate variability (HRV) is mediated by sympathetic

and parasympathetic efferent activities which always interact towards the heart. Our goal was to describe

the link between autonomic nervous system (ANS) and HRV. In 18 epileptic children, ECG data were

obtained before and after implantation of the VNS. HRV was measured by linear and nonlinear parameters

during 50 minute epochs during phase 2 of sleep and deep sleep. Results of the patients were compared with

those of an age and sex matched control group. We were able to confirm that vagus nerve stimulation do not

influence heart rate in children with refractory epilepsy. After the VNS implantation, there is a shift in

sympathovagal balance towards sympathetic predominance in phase 2 of sleep (p=0.177) and also during

deep sleep (p=0.035). This study suggests that left vagus nerve stimulation has little effect on heart rate

variability as measured by nonlinear parameters.

1 INTRODUCTION

The subjects with refractory epilepsy do not respond

to adequate anti-epileptic drugs (30-40%) and they

are prone to autonomic dysfunction. Stimulation of

the vagus nerve is a valuable option for these

subjects. Their heart rate (HR) and autonomic

nervous system (ANS) have been changed due to the

vagus nerve stimulation (VNS). Since the ANS

affects heart rate by continued interaction between

his two branches, sympathetic and parasympathetic,

heart rate variability (HRV) can be used as a

noninvasive tool to show information about the

functional state of the ANS (TASK FORCE, 1996).

Autonomic modulation of heart rate is often

studied by linear parameters, but nonlinear

parameters have opened a new approach for

studying and understanding the

characteristics of

cardiovascular dynamics. They give additional

information about the nonlinear dynamics in the

cardiovascular system, like the quality, scaling and

correlation properties, which cannot be reflected by

standard HRV analysis.

Changes in HR and HRV after the VNS

implementation have been studied using linear

parameters for short-term epochs (5 minutes) by

Setty et al. (1998). Nonlinear HRV analysis on long-

term epochs (50 minutes) of the ECG signals of

children with refractory epilepsy were never done to

our knowledge. Also HRV analysis was never done

in this field for two stages of sleep separately.

The aim of this study is to investigate HRV

parameters of 50 minutes ECG recordings before

and after implantation of the VNS in epileptic

children and to compare these results with the results

of the control group for both sleep stages. Emphasis

will be on how the VNS influences the autonomic

nervous system.

409

Miloševi

c M., Vandeput S., Van Huffel S., Jansen K. and Lagae L..

HEART RATE VARIABILITY ANALYSIS OF CHILDREN WITH REFRACTORY EPILEPSY BEFORE AND AFTER THE VAGUS NERVE STIMULATION.

DOI: 10.5220/0003132504090412

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 409-412

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 METHODS

2.1 Subjects

Originally, 18 subjects (age 2-16 years, 13 males)

with refractory epilepsy participated in this study.

However, only data of 17 subjects could finally be

used. Each subject was measured at two different

time moments, before and long enough after the

implantation of the VNS, being sure of stabilized

conditions.

2.2 Data Collection and Preprocessing

ECG recordings were obtained using two leads with

a sample rate of 250 Hz on a PC based platform.

Two epochs of 50 minutes (one for phase 2 of sleep

and one for deep sleep) were selected manually. The

data length had to be equal in both epochs as well as

for all subjects (epileptic and control) in order to

extract consistent and reliable HRV parameters

(TASK FORCE, 1996). For each recording, a file

containing the consecutive RR intervals, derived

from the ECG signal, was exported and checked

manually before preprocessing. Extra ventricular

beats were replaced by a 20%-filter, meaning that

every RR interval that differs more than 20% of the

previous one is replaced by an average value of the 5

previous and 5 next RR intervals.

2.3 Linear HRV Parameters

All standard HRV parameters are calculated in

agreement with the standards of measurement

proposed by the Task Force of the European Society

of Cardiology and the North American Society of

Pacing and Electrophysiology (TASK FORCE,

1996). As time domain measures, we calculated

mean RR interval, SDNN, SDANN, RMSSD and

pNN50. After resampling the tachogram at 4 Hz

with the use of a cubic spline approximation, power

spectra were obtained by using the Welch method.

The direct current component was removed by

subtracting the mean value of the data set. A sliding

Hamming window of 1024 points with 50% overlap

was used. Three frequency bands were defined: a

very low frequency (VLF) band from 0 to 0.04 Hz, a

low frequency (LF) band from 0.04 to 0.15 Hz and a

high frequency (HF) band from 0.15 to 0.40 Hz.

Within each frequency band the spectral power was

expressed in absolute values (in ms

) as well as in

normalized units (n.u.) which represent the relative

value of each power component in proportion to the

total power minus the VLF component.

Additionally, a low-to-high frequency power ratio

(LF/HF) is calculated to reflect the sympathovagal

balance.

2.4 Nonlinear HRV Parameters

Nonlinear HRV techniques have not been

standardized as the linear ones. They give additional

information about the nonlinear dynamics in the

cardiovascular system which cannot be reflected by

standard HRV analysis. The most commonly used

nonlinear parameters are computed in this study.

The 1/f slope of the log(power) – log(frequency)

plot was obtained from the linear regression from

-4

to 10

-2

Hz (Kobayashi and Musha, 1982). A

slope of -1 is an indication of scaling behaviour.

Fractal dimension is based on the algorithm of

Katz (1988), which describes the planar extent of the

time series. The higher the FD, the more irregular

signal.

Detrended fluctuation analysis quantifies fractal

like correlation properties of the time series and

uncovers short-range and long-range correlations.

The root mean square fluctuation of the integrated

and detrended data are measured within observation

windows of various sizes and then plotted against

window size on a log-log scale (Peng et al., 1996).

The scaling exponent DFA α indicates the slope of

this line, which relates log(fluctuation) to

log(window size). Both the short-term (4–11 beats)

DFA α

and the long-term (>11 beats) DFA α

scaling exponents were calculated. Values of α

around 1 are an indication of scaling behaviour.

Sample entropy measures the likelihood that runs

of patterns that are close will remain close for

subsequent incremental comparisons. SampEn was

calculated according to the formula of Richman and

Moorman (2000) with fixed input variables m = 2

and r = 0.2 (m being the length of compared runs

and r the tolerance level). Higher values of SampEn

indicate a more complex structure in the time series.

Noise titration is currently the only algorithm

available that provides a sufficient test for chaotic

dynamics in noise-contaminated signals (Deng et al.,

2006). It measures chaos by controlled neutralization

with added noise. The output noise limit (NL) > 0

indicates the presence of chaos, and the value of NL

also gives an estimate of its relative intensity. We

calculated two parameters to investigate nonlinear

properties: NLmean (average of NL values

measured in 5 minute windows slid each 30 s) and

NLdr (corresponding detection rate).

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

410

2.5 Statistical Analysis

To compare HRV parameters pairwise between the

different stages of sleep, the nonparametric

Wilcoxon signed rank test was used, as well as to

investigate the before-after VNS differences. For the

comparisons between epileptic and control groups,

Wilcoxon rank sum test was used.

Using those tests, p values were obtained to

examine the similarity between adequate groups for

each HRV parameter. In general, p<0.05 was

considered statistically significant.

3 RESULTS

On all 50 minute segments, during phase 2 of sleep

as well as during deep sleep, all HRV parameters

described in the previous sections were calculated.

beforeVNS afterVNS control beforeVNS afterVNS control

550

600

650

700

750

800

850

900

950

1000

1050

verage

erva

PHASE 2

DEEP SLEEP

Figure 1: Boxplots for average RR intervals for phase 2

(first 3 boxes: epileptic subjects before and after the

implantation of the VNS, and their controls) and deep

sleep (last 3 boxes), respectively.

Detailed information about the mean RR

intervals is shown in Figure 1. Heart rate is

significantly lower for the control group in both

stages of sleep, phase 2 and deep sleep, compared to

the epileptic group, before (p=0.009 and p=0.008,

respectively) and after (p=0.042 and p=0.036,

respectively) implantation of the VNS. On the other

hand, VNS therapy doesn’t affect heart rate

(p=0.554 for phase 2 and p=0.795 for deep sleep).

Since standard deviations for HRV parameters in

the frequency domain are quite high, it is better to

observe values in normalized units. For phase 2 of

sleep, there is an increase of LFn.u. (p=0.209) and a

slight decrease of HFn.u. (p=0.309) due to the VNS

stimulation. There are no remarkable differences

between epileptic (both before and after the VNS)

and control groups, during phase 2. In contrast,

during deep sleep phase, these changes are

statistically significant. Again, HFn.u. (p=0.019)

decreased, and LFn.u. (p=0.019) increased after the

implantation of the VNS in epileptic subjects.

Compared to the control group, even before the

implantation of the VNS, epileptic subjects had

significantly different LFn.u. (p=0.023) and HF.n.u.

(p=0.028) parameters. After the start of the VNS, the

epileptic group is rambled further more from their

controls than before the VNS (p=0.003 for LFn.u.

and p=0.003 for HFn.u.).

beforeVNS afterVNS control beforeVNS afterVNS control

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.1

LF/HF

PHASE 2

DEEP SLEEP

Figure 2: Boxplots for LF/HF ratio for phase 2 sleep and

deep sleep, respectively.

There is an increase of LF/HF ratio in both

stages of sleep after the VNS stimulation (p=0.163

and p=0.049, for phase 2 and deep sleep,

respectively). This increase is higher during deep

sleep. For phase 2, LF/HF ratio is closer to LF/HF

ratio for the control group after VNS (p=0.654),

while for the deep sleep it is higher in both

conditions, before and after VNS (p

preVNS

=0.025 and

postVNS

=0.003). Figure 2 gives boxplots for all

groups for parameter LF/HF.

Coefficients α

and α

increase due to the

implantation of the VNS, especially during deep

sleep, when this increase is statistically significant.

While DFA coefficients of the epileptic group are

getting closer to their controls after the implantation

of the VNS during phase 2 of sleep (p=0.705 and

p=0.945, respectively), in deep sleep their values are

statistically different (p=0.012 and p=0.073,

respectively).

Noise titration is reported by the parameters

NLmean and NLdr. During phase 2 of sleep,

NLmean isn’t statistically different when comparing

pre-VNS versus post-VNS and compared to normal

subjects. In epileptic subjects before the

implantation of the VNS, NLdr is statistically higher

compared to the control group (p=0.046). The same

parameters, during deep sleep, don’t demonstrate

significant differences when comparing pre-VNS

HEART RATE VARIABILITY ANALYSIS OF CHILDREN WITH REFRACTORY EPILEPSY BEFORE AND AFTER

THE VAGUS NERVE STIMULATION

411

versus post-VNS and epileptic versus control

groups.

We found no significant changes in other

parameters due to the VNS, and when compared to

the control group.

4 DISCUSSION

As no remarkable difference was found between pre-

VNS and post-VNS implantation in epileptic

subjects, even when compared to the control group

for the parameters SDNN, SDANN, RMSSD, 1/f

slope, FD and SampEn, no discussion is made.

The major previous studies regarding the effects

of the VNS on HRV were performed during wake-

fulness. Setty et al. (1998) didn’t find any significant

effect of the VNS on the heart rate and heart rate

variability. This study confirmed that VNS does not

influence the heart rate in epileptic children (mean

RR interval in the Figure 1), which is significantly

higher compared to their controls. We found a

reduction of vagal tone after long-term VNS during

night-time, when autonomic control on heart rate

should be mainly sustained by vagal influence

(Pagani et al., 1997). This is however, in contrast to

the study of Kamath et al. (1992), which reported

significant increase in HF component of the power

spectrum.

LF/HF ratio after the implantation of the VNS

stimulation still remains below 1 for most of the

children with refractory epilepsy, meaning that vagal

modulation of the heart rate is still dominant. On the

group level, there are changes in cardiac

sympathovagal balance towards sympathetic

predominance in phase 2 of sleep (p=0.177) and also

during deep sleep (p=0.035) after the VNS

implantation. These findings require further

observations.

In addition, LF/HF and DFA coefficients have

similar pattern of behaviour due to the implantation

of the VNS and compared to the control group. In

other nonlinear parameters, we found no significant

changes due to the implantation of the VNS.

In conclusion, our data indicate that: (a) VNS do

not influence heart rate during phase 2 of sleep and

deep sleep; and (b) VNS affects cardiac

sympathovagal balance.

ACKNOWLEDGEMENTS

Research supported by:

Research Council KUL: GOA Ambiorics, GOA

MaNet, CoE EF/05/006 Optimization in Engineering

(OPTEC), IDO 05/010 EEG-fMRI, IDO 08/013

Autism, IOF-KP06/11 FunCopt; Flemish

Government: FWO: PhD/postdoc grants, G.0302.07

(SVM), G.0341.07 (Data fusion), G.0427.10N

(Integrated EEG-fMRI) research communities

(ICCoS, ANMMM); IWT: TBM070713-Accelero,

TBM070706-IOTA3, TBM080658-MRI (EEG-

fMRI), PhD Grants; Belgian Federal Science Policy

Office: IUAP P6/04 (DYSCO, `Dynamical systems,

control and optimization', 2007-2011); ESA

PRODEX No 90348 (sleep homeostasis); EU: FAST

(FP6-MC-RTN-035801), Neuromath (COST-

BM0601)

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