Dispersion Entropy: A Measure of Electrohysterographic Complexity

for Preterm Labor Discrimination

Félix Nieto-del-Amor

, Yiyao Ye-Lin

Javier Garcia-Casado

, A. Diaz-Martinez

María González Martínez

, R. Monfort-Ortiz

and Gema Prats-Boluda

Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain

Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain

Keywords: Dispersion Entropy, Sample Entropy, Preterm Birth, Electrohysterography, EHG.

Abstract: Although preterm labor is a major cause of neonatal death and often leaves health sequels in the survivors,

there are no accurate and reliable clinical tools for preterm labor prediction. The Electrohysterogram (EHG)

has arisen as a promising alternative that provides relevant information on uterine activity that could be useful

in predicting preterm labor. In this work, we optimized and assessed the performance of the Dispersion

Entropy (DispEn) metric and compared it to conventional Sample Entropy (SampEn) in EHG recordings to

discriminate term from preterm deliveries. For this, we used the two public databases TPEHG and TPEHGT

DS of EHG recordings collected from women during regular checkups. The 10

, 50

and 90

percentiles of

entropy metrics were computed on whole (WBW) and fast wave high (FWH) EHG bandwidths, sweeping the

DispEn and SampEn internal parameters to optimize term/preterm discrimination. The results revealed that

for both the FWH and WBW bandwidths the best separability was reached when computing the 10

percentile,

achieving a p-value (0.00007) for DispEn in FWH, c = 7 and m = 2, associated with lower complexity preterm

deliveries, indicating that DispEn is a promising parameter for preterm labor prediction.

1 INTRODUCTION

A preterm birth (PB) is a high-risk situation and has a

prevalence of up to 10% of all labor cases, affecting

more than 15 million families worldwide (Fuchs et

al., 2004). The consequences of PB affect maternal-

fetal health and is the main cause of mortality in

children under 5 years of age (Leung, 2004). It also

has a high economic cost for national healthcare

systems (Petrou et al., 2019). There are now several

techniques available for preterm birth detection,

mainly the measure of cervical length (O’Hara et al.,

2013) and biochemical markers (Leung, 2004).

However, while these techniques provide a highly

negative predictive value, their positive predictive

values are quite low and do not identify preterm

deliveries (Berghella et al., 2008; Diaz-Martinez et

al., 2020).

https://orcid.org/0000-0003-0050-9360

https://orcid.org/0000-0003-2929-181X

https://orcid.org/0000-0003-1410-2721

https://orcid.org/0000-0002-4605-6048

https://orcid.org/0000-0002-9362-5055

Intrauterine pressure catheter IUPC and

tocodynamometry TOCO are the classical methods of

measuring uterine dynamics. However, the former is

an invasive method and involves risks, while the

latter, although non-invasive, is neither very sensitive

or precise (Euliano et al., 2016). The aim of the

electrohysterographic technique is to deal with these

limitations. The electrohysterogram (EHG) is the

bioelectric signal recording of the muscular activity

of the myometrium. The generation and propagation

of action potentials through a suitable number of

myometrial cells induce uterine muscle contractions

and raise the internal uterine pressure. EHG can

provide essential information on uterine activity

(Devedeux et al., 1993). EHG energy is distributed

between 0.1 and 4Hz and is composed of two waves.

The slow wave (SW) has a period equal to the

duration of contraction and as its bandwidth overlaps

260

Nieto-del-Amor, F., Ye-Lin, Y., Garcia-Casado, J., Diaz-Martinez, A., Martínez, M., Monfort-Ortiz, R. and Prats-Boluda, G.

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination.

DOI: 10.5220/0010316602600267

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS, pages 260-267

ISBN: 978-989-758-490-9

the baseline wander it is difficult to analyze and

extract reliable information from it. The fast wave

(FW) is superimposed on the slow wave signal and

can be divided into two parts according to the

frequency in which it is presented: fast wave low

(FWL), whose frequency peak is between 0.13 and

0.26 Hz and is supposed to be associated with

contraction propagation and fast wave high (FWH),

whose frequency peak is between 0.26 and 0.88 Hz

and is related to uterine cell excitability (Devedeux et

al., 1993; Terrien et al., 2007)

Previous studies have shown that EHG signals

include relevant information on the state of the

uterine electrophysiology and are better able to detect

real preterm cases (Devedeux et al., 1993). It has been

shown that as labor approaches synchronization,

amplitude and predictability increase, complexity is

reduced and there is a shift of frequency content to

high frequencies (Devedeux et al., 1993; Garfield &

Maner, 2007). The literature proposes different non-

linear parameters, such as entropy metrics, to

characterize EHG. These latter reduce their values

when regularity increases (Mischi et al., 2018).

Sample entropy (SampEn) (Richman and

Moorman, 2000) is a widely used metric to

discriminate preterm from term (Garcia-Casado et al.,

2018). Studies in the literature suggest that SampEn

performs best in discriminating term vs preterm

deliveries in EHG recordings among the non-linear

parameters tested (maximal Lyapunov exponent and

correlation dimension) and even outperformed

spectral parameters such as median frequency (Fele-

Žorž et al., 2008; Rostaghi and Azami, 2016). In

addition, SampEn have been used to analyze the

feasibility of using EHG to discriminate women with

threatened preterm labor who gave birth in less than

7 days from those who delivered in more than 7 days

(Mas-Cabo, Prats-Boluda, Perales, et al., 2019).

Rostaghi & Azami (2016) proposed Dispersion

entropy (DispEn) to deal with the limitations of

SampEn, which is sensitive to changes in

simultaneous frequency and amplitude. DispEn is a

computationally efficient measure of the regularity of

time series and outperforms entropy metrics for

characterizing other biomedical signals (Rostaghi and

Azami, 2016). The performance of DispEn has been

explored in different biomedical signals, but in EHG

not yet. Azami et al analyzed magnetoencephalogram

MEG signals to discriminate Alzheimer’s disease

patients from an elderly control group. When

SampEn, permutation entropy (PermEn) (Bandt &

Pompe, 2002), fuzzy entropy (de Luca & Termini,

1993) and DispEn were computed for MEG signals,

DispEn presented the highest separability between

the groups than other entropy metrics(Azami,

Rostaghi, et al., 2016). Kafantaris et al used DispEn

to characterize electrocardiogram ECG signals with

different types of heartbeat (normal-healthy hearbeats,

atrial premature beats and premature ventricular

contractions) and concluded that the algorithm is

capable of producing significantly different DispEn

distributions between the different groups of

heartbeats (Kafantaris et al., 2019). Rostaghi &

Azami studied electroencephalographic signals with

DispEn to test their ability for discriminating focal

and non-focal EEG signals and found that DispEn had

better separability than SampEn and PermEn

(Rostaghi and Azami, 2016). All these case studies

suggest that DispEn is a good estimator of signal

regularity and improves the separability of the groups

under study.

In the present work, we attempted to optimize,

assess and compare DispEn performance with

SampEn to discriminate EHG recordings between

term and preterm deliveries during routine checkups,

when computed in the fast wave high and in the whole

EHG bandwidth.

2 MATERIALS AND METHODS

2.1 EHG Data Bases

Two public EHG data bases available in Physionet

were used for the case study, the “Term-Preterm EHG

Database” (TPEHG) (Fele-Žorž et al., 2008) and the

“The Term-Preterm EHG Dataset with tocogram”

(TPEHGT DS) (Jager et al., 2018) Both have been

widely used in comparative studies on term and

preterm cases and were obtained by the Department

of Obstetrics and Gynecology at the Ljubljana

University Medical Center.

A total of 326 EHG signals with 275 term labor

cases (labor > 37 weeks) and 51 preterm labor cases

(labor < 37 weeks) were recorded during routine

checkups of pregnant women between 22 and 37

weeks of gestation. No induced labor cases or

caesarean deliveries were included.

Thirty-minute EHG signal were recorded in both

databases with the same protocol, consisting of four

disposable electrodes on the woman’s abdomen at an

interelectrode distance of 7cm (Fele-Žorž et al., 2008).

Three bipolar channels, S1, S2 and S3, were obtained

after removing the monopolar EHG recordings, as

shown in Figure 1. Previous studies pointed out that

EHG features from S3 outperform S1 and S2

channels in distinguishing term and preterm

deliveries (Fele-Žorž et al., 2008; Mas-Cabo, Prats-

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

261

Boluda, Garcia-Casado, et al., 2019). Therefore, only

channel S3 was analyzed in the present study. The

bipolar signals were digitized at 20 samples per

second, with a resolution of 16 bits and over a range

of ±2.5 millivolts (Fele-Žorž et al., 2008).

2.2 EHG Signal Characterization

The EHG recordings were filtered in the range 0.1 to

4 Hz by a zero-phase shift 5

order Butterworth

bandpass, since this bandwidth comprises the main

content of EHG signals.

Figure 1: Recording protocol of EHG signals. Modified

from (Jager et al., 2018).

Segments with motion artifacts were visually

identified by experts and discarded from the study.

The criteria adopted to discard EHG sections were:

non-physiological events with a significant abrupt

increase in amplitude compared to basal activity, and

respiratory interference associated with the

appearance of periodic components with frequencies

in the band of 12 and 24cpm (0.2–0.4Hz). 220 term

and 40 preterm EHG recordings were analyzed.

SampEn and DispEn parameters were computed in

120s EHG signal analysis windows with a 50%

overlap (Mas-Cabo, Prats-Boluda, Perales, et al.,

2019) to keep the EHG section at a reasonable

computational cost with the minimal loss of

information (Azami & Escudero, 2018). This analysis

does not require contraction segmentation, which can

be tedious and subjective, and is more suitable for

future clinical use in real-time applications. SampEn

and DispEn were computed for both whole EHG

bandwidth (0.1-4Hz, WBW) and in the Fast Wave

High bandwidth (0.34-4Hz, FWH).

2.2.1 Sample Entropy

SampEn is the negative natural logarithm of the

probability that two similar sequences for m points in

the time series remain similar at the next point, in

which self-matches are not included in calculating the

probability (Richman and Moorman, 2000), so that a

lower SampEn value also indicates more self-

similarity in the time series. SampEn is an

improvement of approximate entropy (Pincus, 1991)

and is a frequently used metric in EHG to distinguish

between term and preterm cases for preterm birth

detection (Fele-Žorž et al., 2008).

SampEn depends on two internal parameters: the

length m of the templates to be compared constructed

from the time series, and a filtering threshold r, the

tolerance of mismatch between the corresponding

elements of the templates. Typically, the value of r is

considered as 0.15 to 0.25 times the value of standard

deviation (SD) of the time series, avoiding most of the

noise present in it. The value of m may be taken

considering that the length of the time series is

between 10

and 10

m+1

, although this latter is not so

restrictive (Xiong et al., 2019).

For the present work r was considered as 0.2

times the value of SD and sweeping m from 2 to 5.

2.2.2 Dispersion Entropy

Rostaghi & Azami (2016) proposed a new irregularity

indicator termed dispersion entropy (DispEn) based

on symbolic dynamics or patterns, transforming data

into a new signal with only a few different patterns

and simplifying the study of dynamic time series to a

distribution of symbol sequences. It was aimed at

dealing with the shortcomings of other entropy

parameters such as SampEn and PermEn. Thus,

unlike other entropy metrics, DispEn is sensitive to

changes in simultaneous frequency and amplitude

values and discriminate diverse biomedical and

mechanical states (Azami and Escudero, 2018).

DispEn depends on the mapping function and

three internal parameters: the length m of templates;

the number of classes c that determine the number of

patterns or classes to be considered in the

computation, and time delay d. It is recommended to

assume d = 1 for the latter parameter and for m and c

consider c

< N, N being the length of the time series.

If c is too low, always with c > 1, signal values are

too far and it leads to being assigned to the same class,

while if c is too high, small variations in the signal

can cause a change of class, making it sensitive to

noise (Rostaghi & Azami, 2016). Taking these

considerations into account, in the present work c was

swept between 3 and 9, m between 2 and 5 and d fixed

to 1. The mapping functions considered were: linear

BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing

262

mapping (linear), normal cumulative distribution

function (NCDF), tangent sigmoid (tansig),

logarithm sigmoid (logsig) and sorting method (sort).

2.2.3 Feature Extraction

A previous study revealed that the 10

and 90

percentiles outperform the 50

percentile of EHG

parameters for differentiating between different

obstetric scenarios, including preterm versus term

delivery (Mas-Cabo et al., 2020). We thus aimed to

consider the contractile activity present in the 120s

window analysis without segmenting the EHG

recordings. For complexity parameters such as

SampEn, the 10

percentile has been found to

perform best in discriminating term and preterm labor

from EHG registers. We therefore worked out the 10

and 90

percentiles of SampEn and DispEn

distributions.

2.2.4 Statistical Analysis

The Wilcoxon Rank-Sum Test was performed to

compare SampEn and DispEn’s ability to distinguish

term and preterm deliveries from EHG recordings in

routine checkups. This is a non-parametric statistical

hypothesis test used to compare two related samples

to assess whether their population mean ranks

differ( < 0.05). It is also suitable for non-normal

distributions such as SampEn and DispEn (see

Figures 2-3).

3 RESULTS

Figures 2 and 3 show the box and whisker plots of the

of 10

, 50

and 90

percentiles of EHG SampEn and

DispEn metrics for term and preterm groups

considering the combination of internal parameter

sweeps mentioned in Materials and Methods. As in

the case of DispEN the different mapping functions

obtained similar results, only the values for sort

mapping are represented.

In Figure 2, it can be seen that preterm group

median values are lower than those of term for the

and 50

SampEn percentiles. This agrees with

other studies in the literature that found the shorter the

time to delivery the higher the predictability of the

EHG signal, and the lower the entropy value. In

addition, the median values seem to decrease as m

passes from 2 to 3.

Table 1 contains the p-values of the Wilcoxon

Rank-Sum Test of SampEn values for distinguishing

term and preterm groups for each entropy parameter

distribution considered. Only the 10

percentile of

SampEn showed statistically significant differences

(p-value < 0.05) between the term and preterm groups,

and the results from the 10

to 90

percentile got

worse as m increased. The p-values were lower in the

FWH than WBW.

Table 1: P-values (Wilcoxon Rank-Sum Test) for SampEn

computed with r = 0.2ꞏSD when comparing different term

and preterm groups. Significant differences (p-value <

0.05) are shaded and the minimum p-value for FWH and

WBW bandwidths are in bold.

m 2 3

FWH

0.00123 0.00964

WBW

0.01071 0.01432

FWH

0.08753 0.28320

WBW

0.20581 0.44725

FWH

0.83969 0.68999

WBW 0.82898 0.81121

Figure 2: Boxplot SampEn distributions from EHG signals in different bandwidths and percentiles.

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

263

Figure 3: Box and whisker plot distributions of DispEn using sorting mapping function computed from EHG signals in

different bandwidths and percentiles.

The distribution of the 10

and 50

DispEn

percentiles had lower median values for the preterm

than term group, as shown in Figure 3, confirming, as

has been stated in the literature, that EHG signal

predictability increases as time to delivery decreases,

but this was not so clear for the 90

percentile. Also,

the DispEn median values decreased as the m and c

values increased. Although Figure 3 only shows the

distribution obtained for sort mapping function, it is

representative for the rest of the cases, because of

their similar trend presented.

Tables 2 and 3 shows the p-values of the

Wilcoxon Rank-Sum Test of DispEn values of the

sort and NCDF mapping function. For the sort

mapping (Table 2), statistically significant

differences between term and preterm groups were

only obtained for the 10

and 50

percentiles in FWH

bandwidth for both m = 2 and 3. However, the lowest

p-value was reached with m = 2 and c = 7 for the 10

percentile. There were thus no internal parameter

combinations for WBW that achieved statistically

significant differences (p-value < 0.05) between term

and preterm groups with this mapping function. In the

NCDF mapping function, p-values were lower than

0.05 in the 10

and 50

percentiles for FWH and in

the 10

percentile for WBW. If Table 2 and Table 3

are compared it can be seen that although significant

statistical results in WBW were reached using NCDF,

the p-values were lower for FWH using sort mapping.

The best discrimination between the groups was at m

= 2 and c = 7, in which DispEn reached its optimum

value in WBW and FWH, the latter having the lowest

p-value.

Comparing the SampEn and DispEn outcomes,

commonly is obtained that lower median values of the

preterm than the term group were obtained and the

median values of the 10

to 90

distributions

decreased as m increased. Both entropy measures

were best able to distinguish between term and

preterm groups (lowest p-values in the test of

Wilconxon) when m = 2 in the 10

percentile.

DispEn outperformed SampEn in discriminating

in FWH (p-value, 0.00007), suggesting a better

ability to separate term and preterm groups. However,

in WBW SampEn reached a lower p-value, 0.01071

than DispEn.

4 DISCUSSION

SampEn is considered as one of the most used non-

linear metrics to discriminate between term and

BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing

264

preterm cases. One of the facts which do this possible

is when SampEn is evaluated for women delivering

prematurely, it is obtained a lower median value than

those of term case (di Marco et al., 2014). This fact is

consistent with the outcomes reaches in this study for

SampEn, and in the same way, the mean values of

DispEn acquired similar distributions. Consequently,

DispEn might be considered as a replacement of

SampEn in the measure of complexity in EHG signals

for discriminate between term and preterm cases. Our

findings reveal that DispEn better distinguishes term

from preterm deliveries than SampEn when

computed on EHG signal sections obtained in routine

checkups, (p-value of 0.00007 vs 0.00123). The

optimization of internal DispEn parameters was

achieved for an embedding dimension of m = 2 and a

number of classes c = 7, for the FWH and 10

percentile. This result agrees with our previous work

that showed the 10

percentile of sample entropy and

Lempel-Ziv non-linear parameters also outperformed

the 50

for distinguishing term and preterm labor and

other obstetric scenarios (Mas-Cabo et al., 2020).

This suggests the possibility of characterizing

contractile activity present in 120s EHG analysis

windows without the need for segmentation.

However, other approaches should also be

considered. The present study was performed using

channel S3 only, which had outperformed channels

S1 and S2 in term/preterm discrimination in previous

studies (Ahmed and Mandic, 2011; Azami et al.,

2019; Azami, Smith, et al., 2016). In future work it is

aimed to extend this study by assessing the

Table 2: P-values (Wilcoxon Rank-Sum Test) for DispEn computed with sort mapping function when comparing different

term and preterm groups. Significant differences (p-value < 0.05) are shaded and the minimum p-value is in

bold.

Table 3: P-values (Wilcoxon Rank-Sum Test) for DispEn computed with mapping function NCDF when comparing different

term and preterm groups. Significant differences (p-value < 0.05) are shaded and the minimum p-value is in

bold.

m c 3 4 5 6 7 8 9

FWH 0.00015 0.00019 0.00012 0.00015 0.00018 0.00015 0.00016

WBW 0.02078 0.02040 0.02732 0.03194 0.01850 0.02797 0.03176

FWH 0.00550 0.00611 0.00414 0.00393 0.00438 0.00594 0.00558

WBW 0.27108 0.27912 0.30853 0.28115 0.28115 0.30314 0.31510

FWH 0.09229 0.13584 0.14194 0.12937 0.15148 0.15148 0.17418

WBW 0.70182 0.66823 0.70691 0.77772 0.80236 0.76897 0.76548

FWH 0.00020 0.00021 0.00017 0.00022 0.00022 0.00019 0.00020

WBW 0.02913 0.02732 0.03362 0.03381 0.02247 0.03051 0.03381

FWH 0.00914 0.00932 0.00655 0.00582 0.00620 0.00866 0.00920

WBW 0.23236 0.28525 0.29780 0.26515 0.27810 0.29044 0.30745

FWH 0.12538 0.17059 0.19301 0.20746 0.21751 0.22792 0.30962

WBW 0.72398 0.71713 0.72912 0.78826 0.77947 0.76199 0.74637

m c 3 4 5 6 7 8 9

FWH 0.00015 0.00014 0.00009 0.00008 0.00007 0.00009 0.00008

WBW 0.10883 0.08668 0.10053 0.09097 0.06042 0.07938 0.07516

FWH 0.00777 0.00692 0.00793 0.00849 0.00726 0.00772 0.00692

WBW 0.41908 0.39326 0.46521 0.41777 0.39579 0.39579 0.39199

FWH 0.07667 0.10783 0.11241 0.09679 0.10486 0.11189 0.11241

WBW 0.76199 0.80060 0.82720 0.85760 0.84684 0.84148 0.86299

FWH 0.00019 0.00016 0.00011 0.00015 0.00012 0.00013 0.00011

WBW 0.11138 0.09588 0.10148 0.08216 0.05856 0.07078 0.06168

FWH 0.01335 0.00938 0.01158 0.01121 0.00908 0.01121 0.01196

WBW 0.41384 0.40734 0.46381 0.40476 0.39707 0.37330 0.36116

FWH 0.09497 0.15811 0.16846 0.18607 0.17274 0.20094 0.21077

WBW 0.77597 0.77947 0.80944 0.85939 0.80060 0.84505 0.80944

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

265

performance of multivariate DispEn and other

multivariate entropy algorithms (Mas-Cabo et al.,

2020) in multichannel EHG databases so as to define

an optimal set of features to develop a preterm labor

predictor.

Other point to be considered is that SampEn has

certain limitations, such as its higher computational

cost than other entropies in similar assessment

conditions and its undefined or unreliable results for

short signals (Azami & Escudero, 2018). For a real-

time or a fast post-processed application in preterm

birth prediction with EHG signals may be useful

provide of an algorithm capable to compute a

measure of complexity of the signal as faster as it is

possible. Thus, DispEn outperform SampEn and

other relative entropy metric in which computational

cost is referred (Azami and Escudero, 2018).

In addition, only a statistical approach of the

separability of probability distributions of term and

preterm registers is taken into account. However, for

summiting a robust preterm labor discriminator, not

only one metric is used. In this way, the

complementation with other metrics related to EHG

signals should be evaluated and so obtain if the

append of DispEn outperforms the scores obtained

with SampEn in similar conditions.

Although this work focused on term vs preterm

discrimination with EHG entropy metrics, EHG can

also be used in other obstetric scenarios (Mas-Cabo et

al., 2020). DispEn may be suitable for the prediction

of labor induction success (Benalcazar-Parra et al.,

2019) or detecting imminent delivery in women with

threatened preterm labor under tocolytic treatment

(Mas-Cabo, Prats-Boluda, Perales, et al., 2019).

5 CONCLUSSIONS

This work assessed the performance of DispEn, a new

complexity parameter in EHG characterization to

distinguish term from preterm deliveries in EHG

recordings picked up during regular checkups. Its

performance was compared with the traditionally

used SampEn. Both entropy metrics computed in

window analyses of 120s show statistically

significant differences (p-value < 0.05) in women

who delivered at term from those who delivered

preterm. DispEn outperformed SampEn

discrimination in FWH, unlike WBW, for which

SampEn reached a lower p-value. In both FWH and

WBW the best discrimination was in the 10

percentile, with the lowest p-value for DispEn in

FWH and internal parameters c = 7 and m = 2, with

lower entropy values for the preterm group.

ACKNOWLEDGEMENTS

This work was supported by the Spanish ministry of

economy and competitiveness, the European

Regional Development Fund (MCIU/AEI/FEDER,

UE RTI2018-094449-A-I00-AR) and the Generalitat

Valenciana (AICO/2019/220).

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