Phase-Rectified Signal Averaging to Evaluate ANS Development in

Premature Infants

Maristella Lucchini

, Devy Widjaja

1,2

, Carolina Varon

1,2

, Katrien Jansen

and Sabine Van Huffel

1,2

Department of Electrical Engineering, ESAT-SCD, KU Leuven

Kasteelpark Arenberg 10, box 2446, 3001 Leuven, Belgium

iMinds Future Health Department, Kasteelpark Arenberg 10, box 2446, 3001 Leuven, Belgium

Department of Pediatric Neurology, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium

Keywords: Phase-Rectified Signal Averaging, Heart Rate Variability, Autonomic Nervous System, Quasi-Periodicities,

Non-Stationary Signals.

Abstract: Aim: Heart Rate Variability (HRV) is determined by the autonomic nervous system (ANS) and a low value

of this parameter is related to neurological pathologies and infants mortality. This study aims to assess the

utility and the advantages of HRV analysis by means of phase-rectified signal averaging (PRSA), a

technique that obtains curves that are useful to determine the development of the ANS in preterm infants,

with less obtrusive monitoring compared to electroencephalography.

Methods: For a preliminary study, 24-hour ECGs were taken in NICU at the University Hospital in Leuven,

from 12 babies: 4 were term, 4 were born preterm but reached a term postmenstrual age, and 4 were

preterm. Heart rate tracks of segments of 27 minutes were extracted and analyzed with the PRSA technique.

The curves obtained were quantified by the slope and by an acceleration/deceleration related parameter

(AC/DC). Two independent analyses on acceleration and deceleration were carried out to visualize the

effects of the sympathetic and parasympathetic system separately. Moreover, the immediate response and

the response after 5 seconds were taken into account.

Results and Conclusion: All the results were compared and validated with traditional HRV parameters. The

results of slope and AD/DC in both types of analysis are promising in providing a simple parameter to

assess neurological development deficiency in order to allow faster and preventive intervention. Further

studies are needed in a larger population.

1 INTRODUCTION

In recent years, the study of heart rate variability

(HRV) has gained growing importance. As a matter

of fact, the cardiac rhythm is a crossroad of

numerous systems of physiological control operation

on different temporal scales: breathing, hormones,

the autonomic nervous system (ANS), etc. The

parasympathetic nervous system has the ability to

decrease the heart rate, while the sympathetic one

usually reacts to acute situations by increasing the

rhythm. Researchers have shown that, in adults, a

stable and predictable HR is usually associated with

ongoing and upcoming pathologies (Rajendra et al.,

2005). This fact opens up a new conception of

homeostasis: in reality our body does not tend to a

fixed situation but keeps its parameter in a range of

fluctuation that allows it to properly respond to

external stimuli (Bauer et al., 2006a).

An interesting aspect of HRV is that it can be

acquired from non-invasive measurements like the

electrocardiogram (ECG). Due to this fact, HRV is

suitable to monitor premature babies, who are

usually very sensitive and unstable, and to

investigate their ANS development, which is a

crucial issue in these kinds of patients. We

hypothesize that babies affected by

underdevelopment of the ANS, will be less capable

of varying their HR in response to external and

internal factors.

The specific aim of this study is to analyze

recordings from term and preterm infants using the

Phase-Rectified Signal Averaging (PRSA) method

(Bauer et al., 2006b). This algorithm is capable of

synchronizing the phase of all periodic components

of a noisy, non-stationary signal with respect to their

203

Lucchini M., Widjaja D., Varon C., Jansen K. and Van Huffel S..

Phase-Rectiﬁed Signal Averaging to Evaluate ANS Development in Premature Infants.

DOI: 10.5220/0004240702030208

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 203-208

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

frequencies and time scales. Moreover, it is a

powerful tool to separately analyze the acceleration

and deceleration of the HR, having thus the

possibility to visualize the influence of the vagal and

sympathetic system separately (Huhn et al., 2011).

In this research, we will focus on the acceleration

and deceleration of the HR, giving a qualitative

description of the obtained PRSA signals and

proposing parameters to evaluate the maturity of the

ANS.

2 DATA AND METHODS

2.1 Data Acquisition and Preprocessing

In this research, 12 ECGs (sampling frequency f

250 Hz) are analyzed: 4 signals are taken from

premature babies (gestational age at birth < 37

weeks), 4 from prematurely born babies that reached

a term postmenstrual age (PMA > 40 weeks) and 4

from term babies (gestational age at birth > 40

weeks), all hospitalized in the pediatric neurology

department at the University Hospital in Leuven.

From 24-hour ECG recordings, a segment of 27

minutes was extracted for each patient. After that,

the tracks were preprocessed in three steps: at first,

the ECG segments were resampled to 1000 Hz using

cubic spline interpolation to gain a better quality

during the analysis, secondly they were filtered at 50

Hz to remove power line interference. In the third

step, the heart beats were extracted using the Pan-

Tompkins algorithm (Pan and Tompkins, 1985).

Based on the time between consecutive beats, it was

possible to calculate the heart rate.

2.2 Description of the PRSA Technique

The heart rate signals were processed using the

PRSA technique, with the aim of compressing the

signal into a shorter sequence without losing any

relevant quasi-periodicities, and eliminating at the

same time non-stationarities, artifacts, and noise.

This technique consists of 3 simple steps outlined in

Figure 1 (Bauer et al., 2006b).

At first, anchor points (AP) are chosen based on

a certain property of the signal x

. In this study,

increases or decreases in the signal are taken into

account, as well as averages of T values of the time

series.

1



)

Figure 1: Illustration of the PRSA technique (Bauer et al.,

2006b). (a) Anchor points are selected from the original

signal x

; here increase events are selected according to

Equation 1. (b) Windows of length 2L are defined around

each anchor point, here are shown the first four anchor

points. (c) The surroundings of many anchor points (all

located in the centre) are shown on top of each other. (d)

The PRSA curve x

resulting from averaging over all

surroundings is shown.

1



)













)













)

We have applied Equations (1) and (2) to see the

beat-to-beat response and (3) and (4) to visualize the

5 seconds (on average 14 beats) response.

Typically half of all points of the signal will be

APs by these definitions. The parameter T sets an

upper frequency limit for the detection of

periodicities, because taking a mean over T samples

is equal to applying a low-pass filter. The capability

of resetting of this method comes from the fact that

when we fix the AP in the increasing (decreasing)

point, they will be in the phase of the steepest ascent

(decent), which means when the phase of the

oscillation is close to 0 (π). Thanks to this process

we can extract the phase information of the

oscillations from the signal itself (Bauer et al.,

2006b). Heart rate accelerations and decelerations of

more than 25% are excluded to suppress errors due

to artifacts.

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

204

Afterwards, windows of length 2L, in this case

L=100 samples, are defined around each AP. APs

where no full surroundings of this length are

available are excluded from the analysis. Since most

of the APs are closer than 2L, most of the windows

will overlap. It is important to make a proper choice

for the parameter L; it should be larger than the

period of the slowest oscillation that one wants to

detect.

Lastly, the windows are aligned at the AP and

the PRSA curve

is obtained by averaging the

aligned windows. Thanks to this average,

components that are not phase synchronized with the

AP, will have zero mean and thus they will cancel

out; instead, the events that have a fixed phase

relationship with the AP in all the 2L windows will

have the same pattern and thus will be kept in the

average.

From each signal, the average was subtracted to

highlight the variation around the mean and to

facilitate the comparison between signals with

different means.

2.3 Measures for the Quantification of

PRSA Curves

For each HR signal, 4 PRSA curves x

were

calculated using Equations (1) to (4), with T=14 for

Equations (3) and (4).

the central peak retains the information of

all the quasi-periodicities of the HR tracks.

Depending on the criteria for the selection of the AP,

the central spike will quantify the mean capacity of

the ANS to accelerate or decelerate the HR.

For the curves obtained with Equations (1) and

(3), the following parameters were calculated:

 AAC: the Average Acceleration Capacity is

the subtraction of the average of the signal

after the AP from the average of the signal

before the AP (Huhn et al., 2011);

 AC: the Acceleration Capacity is defined as

the change around the anchor point that

corresponds to computing the AAC for just

3 points before and after the AP (Bauer et

al., 2006);

 SLOPE_A: the slope of the straight line

connecting the point before and after the

central anchor point is derived.

The same parameters were extracted for the

curves obtained using Equations (2) and (4), taking

into account the deceleration (respectively ADC,

DC, SLOPE_D).

2.4 Time Domain HRV Measures

To validate the results obtained with PRSA, 5 well-

known HRV time domain parameters were

computed (Task Force of The European Society of

Cardiology and The North American Society of

Pacing and Electrophysiology, 1996):

 MeanNN: mean interval between

consecutive QRS complexes (called NN

intervals, i.e. all intervals between adjacent

QRS complexes, obtained from sinus node

depolarizations);

 SDNN: standard deviation of the NN

intervals, i.e. the square root of the

variance. SDNN is a measure for cyclic

components that are present during the

recordings;

 RMSSD: the square root of the mean

squared differences of successive NN

intervals. This measure quantifies

parasympathetic modulation;

 pNN25: the percentage of NN intervals that

differ more than 25 ms with respect to the

preceding NN intervals, compared to the

total number of NN intervals. This is also a

measure for parasympathetic activity;

 SDSD: standard deviation of the differences

between successive NN intervals. SDSD

presents short-term variations and is highly

correlated with RMSSD and pNN25.

3 RESULTS AND DISCUSSION

3.1 PRSA Curves

Figure 2 presents the mean PRSA curves for heart

rate acceleration in beat-to-beat analysis. In Figure

3, the mean PRSA curves for heart rate decelerations

in 14 beats analysis are shown. Both figures clearly

show how the PRSA curves of term babies have

larger amplitudes for all the experiments. In

addition, beat-to-beat curves of terms are more

unstable and unpredictable, whereas the signals of

preterms show a smoother behaviour, indicating less

variation in their HR. The curves obtained from

prematurely born babies that reached a term PMA

show a limited capacity of variation with respect to

term babies of their age.

Figures 4 and 5 and Table 1 show the values of

all the parameters compared among 12 patients for

14 beats analysis and Figures 6 and 7 and Table 2

for beat-to-beat.

Phase-RectifiedSignalAveragingtoEvaluateANSDevelopmentinPrematureInfants

205

In both experiments, it is observable that AAC and

ADC are not suited parameters to describe the curve,

since the difference between the 3 populations is

minimal. This can be explained by the fact that in

beat-to-beat and 14 beats analyses most of the length

of the 2L window contains information not directly

related to the instantaneous acceleration or

deceleration capability of the HR. For this specific

reason, parameters, which take into account the

entire length of the signal, are considered irrelevant

for this type of study.

Figure 2: Mean PRSA curves for acceleration in beat-to-

beat analysis. Premature infants are shown in blue,