PHASE-RECTIFIED SIGNAL AVERAGING FOR THE

QUANTIFICATION OF THE INFLUENCE OF PRENATAL ANXIETY

ON HEART RATE VARIABILITY OF BABIES

Hannelore Eykens

, Devy Widjaja

1,2

, Katrien Vanderperren

1,2

, Joachim Taelman

1,2

Marijke A. K. A. Braeken

, Ren

ee A. Otte

, Bea R. H. Van den Bergh

and Sabine Van Huffel

1,2

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

Kasteelpark Arenberg 10, box 2446, 3001 Leuven, Belgium

IBBT-K.U.Leuven Future Health Department, Kasteelpark Arenberg 10, box 2446, 3001 Leuven, Belgium

Department of Developmental Psychology, Universiteit van Tilburg

Warandelaan 2, PO box 90153, 5000 LE Tilburg, the Netherlands

Keywords:

Phase-rectiﬁed Signal Averaging, Quasi-periodicities, Non-stationary Signals, Tachogram, Heart Rate

Variability, Prenatal Anxiety, Autonomic Nervous System.

Abstract:

The autonomic nervous system (ANS) modulates heartbeat intervals responding to inputs from its different

branches, resulting in periodicities that occur on different time scales. Internal and external perturbations are

continuously interrupting the periodic behavior, making the heartbeat intervals quasi-periodic. Phase-rectiﬁed

signal averaging (PRSA) is a technique to detect those quasi-periodicities in noisy, non-stationary signals,

like tachograms. The method compresses the tachogram in shorter curves based on internal information,

and provides information on the deceleration and acceleration capacity of the heart. In this study, the PRSA

technique is investigated as a novel signal processing technique for the analysis of heart rate variability (HRV)

of babies. In this way, the effect of stress and anxiety during pregnancy on the ANS of the baby is analyzed.

First, the PRSA curves are obtained for each baby and different measures that characterize these curves are

deﬁned. Next, these measures are linked to the anxiety level of their mothers during pregnancy. Only little

inﬂuence of the anxiety level of the mother on the HRV of the baby is found.

1 INTRODUCTION

Stress and anxiety during pregnancy can lead to a less

optimal development of the fetus, which can result in

cognitive, emotional and behavioral problems in later

life (O’Connor et al., 2003; Van den Bergh and Mar-

coen, 2004). Prenatal stress may also cause infants to

suffer from a less mature autonomic nervous system

(ANS) and an increased sensibility to stress (Van den

Bergh et al., 2005). The activity of the ANS can be

evaluated based on the variability of the heart rate,

which is modulated by the interacting sympathetic

and parasympathetic branches. To assess heart rate

variability (HRV), the R peaks from the electrocardio-

gram (ECG) are detected and the intervals between

successive peaks (RR intervals) are plotted in time,

resulting in a tachogram. Based on this tachogram,

several measures that quantify HRV are deﬁned. By

linking these HRV measures of the babies to the anxi-

ety level of the mothers, the inﬂuence of prenatal anx-

iety on the ANS of the babies can be examined. This

study is part of a larger project that aims at investi-

gating the relation between stress and anxiety during

pregnancy and the development and outcome of the

baby. In a previous phase, the relation between the

anxiety and the ANS of the pregnant women was an-

alyzed (Taelman et al., 2010).

The ANS modulates the heart rate by continu-

ously reacting to the inputs of the heart, lungs and

blood vessels. These heart rate modulations due to

intrinsic regulation processes occur on different time

scales, which can be evaluated with phase-rectiﬁed

signal averaging (PRSA). PRSA is a technique that

detects quasi-periodicities in non-stationary signals,

like the tachogram (Bauer et al., 2006b). It com-

presses the tachogram into a shorter sequence, keep-

ing all relevant quasi-periodicities but eliminating

non-stationarities, artifacts and noise. The resulting

163

Eykens H., Widjaja D., Vanderperren K., Taelman J., A. K. A. Braeken M., A. Otte R., R. A. Van den Bergh B. and Van Huffel S..

PHASE-RECTIFIED SIGNAL AVERAGING FOR THE QUANTIFICATION OF THE INFLUENCE OF PRENATAL ANXIETY ON HEART RATE

VARIABILITY OF BABIES.

DOI: 10.5220/0003702101630168

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 163-168

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

PRSA curve characterizes the deceleration and accel-

eration capacity of the heart.

This paper focuses on the relevance of PRSA as a

technique to measure heart rate variability. However,

as other measures, deﬁned in both the time and fre-

quency domain, are generally used to quantify HRV

(Task Force of The European Society of Cardiology

and The North American Society of Pacing and Elec-

trophysiology, 1996), we will brieﬂy compare the

most commonly used time domain HRV measures

with the PRSA technique as well.

2 DATA

The data for this study have been measured at Tilburg

University as a part of the EuroSTRESS project that

investigates the inﬂuence of stress and anxiety dur-

ing pregnancy on the cardiorespiratory system of the

women and on the development of the baby. The State

Trait Anxiety Inventory (STAI) (Spielberger et al.,

1983) is used as a psychological measure to quan-

tify the anxiety during the ﬁrst trimester of the preg-

nancy. Based on the STAI score, subjects belong to

a low (STAI ≤ 28), moderate (28 < STAI < 40) or

high (STAI ≥ 40) anxiety group. To assess the devel-

opment of the baby, the electrocardiogram and elec-

troencephalogram of the baby have been recorded at

two ages. During the acquisition, an auditory odd-

ball paradigm was presented. This paradigm consists

of ﬁve series of stimuli, in which a frequent stimulus

(1000 Hz tone) was randomly alternated with three

different deviant stimuli. The sampling frequency is

512 Hz; an ECG signal during one stimulus sequence

has a length of about 150 s. 76 babies of 2 to 4 months

old are included in the study.

3 METHODS

The basic principle of the PRSA technique consists

in deﬁning anchor points, selecting windows around

these anchor points, aligning the windows, and av-

eraging over all surroundings. Next, measures are

chosen to describe the resulting PRSA curves. In or-

der to interpret the PRSA measures, some traditional

HRV measures are calculated to make the compari-

son. The results are statistically evaluated using the

Spearman’s correlation coefﬁcient and the Wilcoxon

rank sum test.

3.1 Description of the PRSA Technique

Figure 1 outlines the basic steps of the PRSA tech-

nique, starting from the tachogram (Kantelhardt et al.,

2007). In the ﬁrst step, anchor points are selected

according to a certain property in the tachogram x

Possible selection criteria are based on an increase

and decrease of a sample with respect to the previous

sample. The general deﬁnition of anchor points that

is used in this study, compares averages of a period of

T values of the tachogram:

T −1

∑

j=0

i+ j

∑

j=1

i− j

(1)

T −1

∑

j=0

i+ j

∑

j=1

i− j

(2)

The parameter T sets an upper frequency limit for

the periodicities that can be detected and functions as

a low pass ﬁlter. For T = 1, no ﬁlter is applied; all

increases or decreases in the signal are selected as an-

chor points. In Figure 1, all increase events are de-

ﬁned as anchor points, according to Equation (1) with

T = 1.

In the second step, windows (surroundings) of

length 2L are deﬁned around each anchor point. The

parameter L should exceed the period of the slowest

oscillation that is of interest.

Finally, the surroundings of all anchor points are

aligned to each other and the PRSA curve ¯x

is ob-

tained by averaging over all windows. With this av-

eraging procedure, non-periodic components that are

not in phase with the anchor points are cancelled out,

leaving only periodicities and quasi-periodicities that

have a ﬁxed phase relationship with the anchor points.

In this work, the symbols PRSA

and PRSA

are

used to indicate PRSA curves based on increases or

decreases in the tachogram.

Figure 1: Illustration of the PRSA technique (Kantelhardt

et al., 2007). (a) Anchor points are selected, based on in-

creases in the tachogram; (b) windows are deﬁned around

each anchor points; (c) all anchor points are moved on top

of each other, resulting in the alignment of all windows; (d)

the phase-rectiﬁed signal average ¯x

is obtained by averag-

ing over all windows.

The center of the PRSA curve ¯x

is the average

of the tachogram at all anchor points. The measures

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

164

deﬁned to quantify the curve, use this central point

as reference. Therefore, a recalibration step shifts the

curve such that the amplitude of ¯x

equals 0 ms.

3.2 Measures for Quantiﬁcation of the

PRSA Curve

In ¯x

all periodicities are superposed; the central peak

of the PRSA curve contains the contributions from

all the (quasi-)periodicities of the original tachogram.

The deﬂection at the center of the PRSA curve de-

pends on the deﬁnition of anchor points. For Equation

(1), the central spike quantiﬁes the average capacity

of the ANS to decelerate the heartbeat (deceleration

capacity DC). DC [ms] is calculated from four points

around the center of PRSA

, as shown in Figure 2 and

Equation (3), and proved its use as a better predictor

of mortality after myocardial infarction than other tra-

ditional HRV measures (Bauer et al., 2006a):

DC =

¯x

+ ¯x

− ¯x

−1

− ¯x

−2

/4 (3)

Figure 2: Illustration of the calculation of measure DC for

PRSA

and T = 1 for the tachogram of a random baby of

2 months old.

For the deﬁnition described in Equation (2), the

acceleration capacity (AC) is used to quantify the cen-

tral deﬂection of PRSA

Observation of the PRSA curves showed that the

curves of different babies not only differ from each

other in values for DC and AC. Also the amplitude,

oscillations and morphology for the whole curve vary

between different subjects. In this study, additional

measures are selected to describe the PRSA curve

as precisely as possible. In this way, differences in

curves between babies can be quantiﬁed and analyzed

to examine the link with the STAI score of the moth-

ers.

• Peak-to-peak: distance [samples] and difference

in amplitude [ms] between the ﬁrst peak before

and the ﬁrst peak after the center of the curve;

• Area-Under-Curve (AUC) [ms

]: area under the

PRSA curve ¯x

in the predeﬁned intervals k =

[−20 : 0] and k = [0 : 20];

• Skewness [-]: measure for the lack of symmetry

of the distribution of the whole PRSA curve. Zero

skewness indicates a symmetry around the mean.

Positive or negative skewness indicates a right or

left tail, respectively.

• Excess kurtosis [-]: measure for the ‘peakedness’

of the distribution of the whole PRSA curve. Neg-

ative values indicate ﬂatness, while positive val-

ues indicate a more peaked distribution.

3.3 Time Domain Measures of HRV

In addition to the analysis of the PRSA curves, some

traditional time domain measures for HRV are com-

puted (Task Force of The European Society of Cardi-

ology and The North American Society of Pacing and

Electrophysiology, 1996):

• SDNN [ms]: standard deviation of the RR inter-

vals. This measure indicates which cyclic compo-

nents are present during the recordings;

• RMSSD [ms]: root mean square of successive RR

differences. RMSSD is a measure of parasympa-

thetic modulation;

• pNN25 [%]: the percentage of RR interval differ-

ences that are greater than 25 ms. Like RMSSD,

pNN25 quantiﬁes parasympathetic activity.

3.4 Statistical Analysis

The correlations between the STAI score of the moth-

ers and the PRSA and HRV measures for all infants,

are calculated using Spearman’s correlation coefﬁ-

cient. This method aims at detecting a monotonic

relation between two distributions. In order to in-

terpret and compare the deﬁned PRSA measures, the

correlation between the PRSA measures and the time

domain HRV measures are computed as well. The

Wilcoxon rank sum test is used to compare the high

anxiety group and the low anxiety group. It is a non-

parametric test to check whether two data sets are

coming from the same distribution. The signiﬁcance

level for rejecting the null hypothesis is p = 0.05.

4 RESULTS AND DISCUSSION

Four PRSA curves are obtained for each tachogram

for surroundings of length 2L = 100 samples, based

on the four deﬁnitions used in this study: anchor

points linked to both increases and decreases for both

T = 1 and T = 10. For each baby, the ECG was

recorded during ﬁve stimuli sequences. The PRSA

PHASE-RECTIFIED SIGNAL AVERAGING FOR THE QUANTIFICATION OF THE INFLUENCE OF PRENATAL

ANXIETY ON HEART RATE VARIABILITY OF BABIES

165

Table 1: Mean ± standard deviation of kurtosis for all PRSA curves, divided in three anxiety groups based on the STAI score

of the mother (low, moderate and high anxiety groups). n is the number of babies in the anxiety group; ρ is the Spearman

correlation coefﬁcient and p is its corresponding p-value; p

L−H

is the resulting p-value for the comparison between babies of

low and highly anxious women.

Low (n = 21) Mod. (n = 41) High (n = 14) ρ p p

L−H

T = 1 PRSA

-0.50 ± 0.93 -0.23 ± 0.65 -0.16 ± 0.75 0.214 0.078 0.022

T = 1 PRSA

-0.76 ± 0.84 -0.40 ± 0.82 -0.47 ± 0.68 0.120 0.326 0.022

T = 10 PRSA

-1.03 ± 0.28 -0.61 ± 0.51 -0.66 ± 0.33 0.268 0.026 <0.001

T = 10 PRSA

-0.99 ± 0.41 -0.75 ± 0.41 -0.72 ± 0.41 0.186 0.127 0.034

measures calculated for the ﬁve ECG signals are av-

eraged for each baby.

As mentioned before, Figure 2 shows the PRSA

curve of one baby, based on anchor points linked to

all increase events in the tachogram (T = 1). Figure 3

shows the PRSA curve for the same baby, according

to T = 10. In this way, a much smoother PRSA curve

is obtained, compared to Figure 2. Also the absolute

values of the amplitudes of the positive peak for k >0

and negative peak for k <0 are higher. This is be-

cause the parameter T functions as a low pass ﬁlter,

only selecting anchor points by comparing series of T

samples. One sudden increase in a series of decrease

events will not be selected; only average increases in

the tachogram will give rise to anchor points.

Figure 3: PRSA

curve (T = 1) for the tachogram of a ran-

dom baby of 2 months old.

4.1 Inﬂuence of Prenatal Anxiety on

PRSA Measures of Babies

The deﬁned PRSA measures are linked to the STAI

score to assess the inﬂuence of prenatal anxiety of the

pregnant women on the ANS of their babies. Signiﬁ-

cant differences between the anxiety groups are only

found for the measure kurtosis for all four deﬁnitions

of PRSA curves. Table 1 shows all statistically sig-

niﬁcant results. Babies with highly anxious mothers

show higher kurtosis (but still negative) than babies

with lowly anxious mothers. Kurtosis measures the

degree of peakedness of the probability distribution of

a variable. A distribution with negative kurtosis has a

wider peak and is said to be ﬂat. The lowest p-value

L−H

= 9, 58e

−4

) is found for the PRSA curve corre-

sponding to increases in the average of 10 consecutive

RR intervals (PRSA

and T = 10). The correspond-

ing boxplots are shown in Figure 4.

Figure 4: Boxplot for kurtosis [-] (PRSA

and T = 10).

Besides this result, some remarkable but statisti-

cally not signiﬁcant results are also presented. DC

and AC quantify the central part of the PRSA curve

around increase and decrease events respectively. A

lower value for these two measures for babies of

highly anxious mothers was observed for all four

PRSA curves, though statistically not signiﬁcant as

mentioned before. Lower values indicate a reduced

capacity of the ANS to quickly adjust the heartbeat.

One remark has to be made; the measure of anxi-

ety used in this study, is based on the state anxiety in

the ﬁrst trimester of the pregnancy. This type of anx-

iety manifests itself as a transitory, emotional state.

The trait anxiety on the other hand, is a relatively sta-

ble aspect of the personality. By using this form of

anxiety to quantify the stress and anxiety level of the

mothers instead of the state anxiety measured at one

moment in time, the analysis of the effect of prenatal

anxiety on the babies might improve.

4.2 Link of PRSA with Time Domain

HRV Measures

In order to link the deﬁned PRSA measures with the

traditional HRV measures, the correlations between

PRSA and some time domain HRV measures are

computed. All PRSA measures, except for skewness,

show signiﬁcant correlations with SDNN, RMSSD

and pNN25. However, we will focus on the exist-

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

166

Table 2: Spearman correlation coefﬁcients between kurtosis

of the PRSA curves and the time domain HRV measures

(∗ : p < 0.05,† : p < 0.005,‡ : p < 0.001).

SDNN RMSSD pNN25

T = 1 PRSA

-0.006 0.328 † 0.390 ‡

T = 1 PRSA

-0.068 0.371 † 0.451 ‡

T = 10 PRSA

-0.015 0.170 0.214

T = 10 PRSA

0.097 0.279 ∗ 0.342 †

ing correlations of kurtosis of the PRSA curves as

this measure showed to differ signiﬁcantly between

anxiety groups in the previous section and it is not

straightforward to interpret these differences. Table 2

shows the correlation coefﬁcients between the kurto-

sis of the PRSA curves and the time domain measures.

Positive correlations are found between kurtosis and

RMSSD and pNN25. Both of these time domain

measures are linked with parasympathetic modula-

tion, suggesting that the kurtosis of the PRSA curves

might be related with the parasympathetic activity as

well. However, future research must focus on the link

between the deﬁned PRSA measures and the ongo-

ing physiological processes. Nevertheless, we want

to stress that the deﬁned PRSA measures are useful

as kurtosis is able to distinguish between the effect of

high and low anxiety during pregnancy on the ANS

of the babies. In our study this was not possible with

the traditional HRV measures.

5 CONCLUSIONS

Quasi-periodicities in the human heart rate reﬂect

the different regulation processes of the ANS. The

PRSA method is a suited technique for detection

of quasi-periodicities in non-stationary data like the

tachogram. Moreover, PRSA offers the possibility to

study the deceleration and acceleration capacity of the

heart, which might provide more insights into cardiac

autonomic regulation processes.

The inﬂuence of the stress and anxiety of pregnant

mothers, quantiﬁed by the STAI score, on the HRV

is investigated by evaluating the PRSA curves. Only

few signiﬁcant results are found, all corresponding to

the kurtosis. Although kurtosis seems to differ signif-

icantly between babies with low and highly anxious

mothers, the interpretation of this measure is unclear.

The inﬂuence of the state anxiety of mothers on

the HRV of babies, using the PRSA technique, is

rather small. Nevertheless, PRSA is a promising sig-

nal processing tool for assessing information about

the capacity of the ANS to quickly adjust its heart

rate. A suggestion of further reseach has been made:

by using a different psychological measure for stress

and anxiety, better and more reliable results may be

found.

ACKNOWLEDGEMENTS

Research supported by:

• Research Council KUL: GOA MaNet;

• D. Widjaja and K. Vanderperren are supported by

an IWT PhD grant;

• Belgian Federal Science Policy Ofﬁce: IUAP

P6/04 (DYSCO).

The scientiﬁc responsibility is assumed by its authors.

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