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Research Article

Monitoring Fetal Heart Rate during Pregnancy:

Contributions from Advanced Signal Processing

and Wearable Technology

Maria G. Signorini,1Andrea Fanelli,2and Giovanni Magenes3

1Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, piazza Leonardo da Vinci 32,

20133 Milano, Italy

2ComputationalPhysiologicalandClinicalInferenceGroup,1433rdStreet,Apt.1,Cambridge,MA02141,USA

3Dipartimento di Ingegneria Industriale e dell’Informazione, University of Pavia, Via A. Ferrata 1, 27100 Pavia, Italy

Correspondence should be addressed to Maria G. Signorini; mariagabriella.signorini@polimi.it

Received July ; Revised October ; Accepted November ; Published January

Academic Editor: Mihaela Ungureanu

Copyright © Maria G. Signorini et al. is is an open access article distributed under the Creative Commons Attribution

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

cited.

Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their conditions, thus

providing necessary interventions in time. Both these two objectives can be achieved by integrating technological development with

methodological tools, thus allowing accurate classication and extraction of useful diagnostic information. e paper is focused

on monitoring procedures applied to fetal heart rate variability (FHRV) signals, collected during pregnancy, in order to assess fetal

well-being. e use of linear time and frequency techniques as well as the computation of non linear indices can contribute to

enhancing the diagnostic power and reliability of fetal monitoring. e paper shows how advanced signal processing approaches

can contribute to developing new diagnostic and classication indices. eir usefulness is evaluated by comparing two selected

populations: normal fetuses and intra uterine growth restricted (IUGR) fetuses. Results show that the computation of dierent

indices on FHRV signals, either linear and nonlinear, gives helpful indications to describe pathophysiological mechanisms involved

in the cardiovascular and neural system controlling the fetal heart. As a further contribution, the paper briey describes how the

introduction of wearable systems for fetal ECG recording could provide new technological solutions improving the quality and

usability of prenatal monitoring.

1. Introduction

Monitoring biomedical signals, through measurement, quan-

tication, evaluation, and classication of signal properties,

is one of the primary tools for investigating the evolution

of disease states. e overall architecture of a monitoring

system has to combine technological tools with signal analysis

methods in order to extract useful information to identify

patient’s condition.

Inside these procedures, it is very important to select

processing methods that can enhance pathophysiological

signal properties, thus linking parameters to physiological

events (and maybe to physical quantities).

Traditional monitoring systems received a fundamental

improvement by new technological devices allowing longer

and deeper data collection as well as by advanced clinical

tools for data interpretation.

In recent years, the development of dynamical system

analysishasledtotheintroductionofalargeamountofsignal

processing techniques aimed at the extraction of parameters

from experimental time series, thus enhancing new informa-

tionaboutthecharacteristicsofthesystemgeneratingthe

time series. In most cases, however, an accurate model of the

generating system is unknown or too complex and the output

signal is the main available information about the system

itself.

A typical example is the cardiovascular system, where

the main way to investigate heart function consists of the

analysis of heart rate variability signal (HRV). It has been

shownthatHRVsignalcanberelatedtotheactivityofseveral

Hindawi Publishing Corporation

Computational and Mathematical Methods in Medicine

Volume 2014, Article ID 707581, 10 pages

http://dx.doi.org/10.1155/2014/707581

Computational and Mathematical Methods in Medicine

physiological control mechanisms of dierent nature. eir

interaction produces changes in the beat rate assuring the

system controlling heartbeats reacts eciently to dierent

incoming stimuli. HRV variance is related to changed con-

ditions of heart activity. Frequency domain analysis of the

HRV signal provides quantitative and noninvasive measures

of the activity of the autonomic nervous system (ANS) [].

A linear modeling approach is adopted to quantify both the

sympathetic and parasympathetic control mechanisms and

their balance through the measure of spectral low and high

frequencycomponents(LFandHF).esameapproach

can extract parameters related to the heart and to the

cardiovascular control even from systolic and diastolic values

in arterial blood pressure (ABP), on a beat-to-beat basis

[].

Nevertheless, even if the HRV analysis through classical

linear methods provides the quantication the ANS regu-

lating action in the short period [], the linear approach

cannot explain the whole information carried by beat-to-

beat variability []. Results on HRV signal analysis show that

its dynamic behavior also involves nonlinear components

that contribute to the signal generation and control [,].

Signal structure appears erratic but it presents abrupt changes

and patterns in which a more regular behavior appears. To

investigate the erratic components of the cardiac rhythms and

to assess nonlinear deterministic phenomena aecting HRV

signal, both in short and long temporal windows, nonlinear

signal analysis has demonstrated its usefulness [].

Intheeldoffetalheartratemonitoringduringpreg-

nancy, linear time and frequency techniques were tradition-

ally adopted. Fetal HR monitoring is a challenging procedure

forpeopleworkingintheobstetriceld,inordertocheckif

the fetus is and remains in a wellbeing state as the pregnancy

develops.

e most employed diagnostic examination in the clinical

practice is cardiotocography (CTG). CTG combines fetal

heart rate (FHR) measurement, obtained by means of a

Doppler ultrasound probe and uterine contraction, recorded

through an abdominal pressure transducer. During preg-

nancy, each woman undergoes one or more ambulatory

monitoring tests and, in the last pregnancy trimester and/or

in case of suspect that risky condition can take place,

monitoring frequency can increase to weekly or even daily.

We can certainly state that the total CTG recording amount,

in our country, is about million per year and reaches several

million exams in EU countries.

CTG is universally accepted in the clinical practice

and it is recognized as one of the most information rich

among noninvasive diagnostic tests for prenatal monitoring.

Nevertheless, the FHR signal is usually analysed by detecting

and measuring morphological characteristics whose clinical

relevance is established mainly by eye inspection. is repre-

sents a strong limitation because the application of subjective

and qualitative methods lacks reliability and depends on the

physician experience.

Moreover, the CTG exam needs a hospital context to be

performed both as an expert clinician only can produce the

clinical report and the technology the system requires for

signal recording.

Onecanstatewithsomecondencethatthetech-

niques used in the prenatal diagnosis for FHR analysis did

not experience a growth rate as the knowledge did, con-

cerning physiological mechanisms and the availability of

methodological tools with clearly demonstrated investigation

abilities.

e introduction of quantitative evaluation of both linear

and nonlinear indices increases the diagnostic power and

reliability of antepartum monitoring.

e paper presents results obtained by applying both

linear and nonlinear quantitative analysis to fetal heart rate

(FHR)signalscollectedinnormalandintrauterinegrowth

restricted (IUGR) fetuses ( + subjects).

Finally, as a further contribution, the paper briey

describes the simultaneous development of a new wearable

monitoring system allowing comfortable collection of fetal

ECG and HRV signals in long periods. is new device

named Telefetalcare is equipped with the analysis tools

developed for the fetal HR analysis and described in this

paper, and can provide further improvements to pre-natal

diagnostic system tools.

2. Materials and Methods

2.1. FHRV Recording. FHRV recordings were collected at the

Azienda Ospedaliera Universitaria Federico II, Napoli, Italy.

Signals were recorded by means of a Hewlett Packard CTG

fetal monitor, linked with a PC computer through a USB port.

e HP fetal monitors use an autocorrelation technique to

compare the demodulated Doppler signal of a heartbeat with

the next one.E ach Doppler signal is sampled at Hz ( ms).

e time window over which the autocorrelation function is

computed is . sec, corresponding to a FHR lower bound of

bpm. A peak detection soware then determines the heart

period (the equivalent of RR period) from the autocorrelation

function. With a peak position interpolation algorithm, the

eective resolution is better than ms.

Due to historical reasons, almost all commercially avail-

ablefetalCTGmonitorsdisplayonlythefetalheartrate

expressed in number of beats per minute (bpm) and do not

oer the series of interbeat intervals, usually employed in

HRV analysis.

e HP monitor produces a FHR value in bpm every

msec. In the commercially available system, the PC

reads consecutive values from the monitor every . sec

and determines the actual FHR as the average of the

values (corresponding to an equivalent sampling frequency

of . Hz). We modied the soware in order to read the FHR

at Hz (every . sec). e choice of reading the FHR values

each . sec represents a reasonable compromise to achieve

an enough large bandwidth (Nyquist Frequency Hz) and an

acceptable accuracy of the FHR signal. An example of CTG

recording is shown in Figure , where both the FHR and the

uterine contractions are plotted as functions of time.

e whole set of recordings was composed of subjects

( healthy and IUGR). Both groups were dened “a

posteriori,” aer delivery, on the basis of standard parameters

(Apgar scores, weight, abdominal circumference): IUGR

Computational and Mathematical Methods in Medicine

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05 10152025

F : Example of CTG graph. e upper trace is fetal heart rate signal obtained by a Doppler ultrasound probe; the baseline is drawn and

the arrows represent the detected accelerations. e lower tracing is the toco signal (uterine contractions). Time units are in minutes.

T : Detailed summary of the two groups of fetuses.

Population details Healthy IUGR

Number

Mother age (years) . ±. . ±.

Gestational age at CTG

recording (days) . ±. . ±.

Gestation age at delivery

(days) . ±. . ±.

Weight of the baby aer

delivery g ± g g ± g

Delivery mode % spontaneous

% caesarean

.% spontaneous

.% caesarean

fetuses were selected by weight below the th percentile for

their gestational age and abdominal circumference below the

th percentile.

Table summarizes population details. All recordings

were made in a controlled clinical environment, with the

pregnant woman lying on a bed. e average length of the

recordings was 2450±724 sec for healthy and 3418±1033 sec

for IUGR group.

2.2. Time and Frequency Domain FHR Analysis

2.2.1. Baseline, Accelerations, and Decelerations. Interpreta-

tion of the heart rate pattern is usually performed by the

physician who analyses the deviations of the signal from an

imaginary line, the baseline. He/she hypothetically constructs

it as a running average of the heart rate. Accelerations and

decelerations are dened as deviations from the baseline,

and more than one quantitative denition is available. In the

construction of an automated system for the evaluation of the

CTG recordings, a reproducible determination of the baseline

is a fundamental starting point. Several attempts in this

directionhavebeenmadestartingfromtheworkofDaweset

al. [];theapproachwefollowedwasthatsuggestedbyMantel

et al. [] (an example of baseline is shown in Figure ). e

algorithm is very complex, and a full description can be found

in the cited reference.

Accelerations and decelerations are deviations of the fetal

heart rate from the baseline lasting a sucient amount of time

(accelerations are positive deviations, decelerations negative).

ey are correlated with the normal activities of the fetus,

who “trains,” moves, and exercises to breathe. Decelerations

are usually correlated with uterine contraction. Unfortu-

nately, dierent quantications of the words “deviations”

and “sucient” led each medical school to develop its own

method to evaluate, by means of a ruler, these quantities on

the monitoring strip. We applied a quantitative procedure not

only fully consistent with the denition of Mantel et al. [],

but also holding the suggestions of Arduini et al. [].

Classical FHR linear indices are truly time domain

measures. In the following, interbeat sequences (),=

1,...,,willbeusedinsteadofheartratesequences() in

beats per minute, usually employed in cardiotocography: they

are computed as () = 60000/()ms. Moreover, in order

to be compatible with previous works (Arduini et al. []) we

also computed some indices on the basis of the undersampled

time series 24() = 60000/24() ms, =1,...,/5obtained

by taking 24() as the average of ve consecutive FHR values

of ().

2.2.2. Long Term Irregularity. Long Term irregularity (LTI)

was the rst index ever introduced; it was proposed by De

Haan et al. []. It is usually computed on a three-minute

segment of interbeat sequence in milliseconds. We excluded

from the computation large accelerations and decelerations,

as suggested by Arduini et al. [], to avoid deviations caused

Computational and Mathematical Methods in Medicine

by spurious measures of variability. e three minutes, aer

the removal of the undesired parts, must contain, at least, a

continuous segment of seconds.

Given a signal 24() with ∈[;], LTI is dened as the

interquartile range [1/4;3/4] of the distribution 24() with

∈[;−1]and 24() = 242() + 242( + 1).

2.2.3. Short Term Variability. Short term variability (STV)

quanties FHR variability over a very short time scale, usually

on a beat-to-beat basis. We refer to the denitions provided

by Dalton et al. [] (even if we used a scale factor of ) and

by Arduini et al. []. By considering one minute of interbeat

sequence, 24() in ms, =1,...,24, we dened STV as

STV =mean[

24( + 1) − 24()

]𝑖

=∑23

𝑖=1

24 (+1

)−

24 ()

23 ,()

where 24() is the value of the signal () taken each . sec

(i.e., once each ve samples).

2.2.4. Interval Index. Historically, Interval Index (II) was

introduced just aer LTI and it is certainly one of the most

used variability indices. It was proposed by Yeh et al. []as

a long term variability statistic; we adopted the formulation

used by Arduini et al. [],

II =std 24 (+1

)−

24 ()

STV , =1,...,23. ()

2.2.5. Power Spectral Analysis of Fetal HRV. Considering the

FHRV signal as controlled by the ANS, as it happens in adult

subjects, it could be of primary importance to own a tool

quantifying its development during pregnancy. Literature

reports several examples on this subject. e ANS is still

developing, if not as the anatomic growth as in the regulatory

activity which increases in time with the system maturation.

Estimation of the power spectral density (PSD) in the

FHR signal provides parameters related to the ANS activity.

Frequency domain FHR analysis adopt both the direct

estimation of the periodogram and the autoregressive power

spectrum estimation.

InfetalHRanalysisitiscustomarytoconsiderthree

frequency bands, Low Frequency (LF), Movement Frequency

(MF), and High Frequency (HF) power components as well

as the ratio LF/(MF + HF) [], instead of the bands usually

adopted for standard HRV analysis [].

Low Frequency contributions (LF: .–. Hz) can

be associated with the sympathetic control and vasomotor

activity. HF is basically driven by respiration mediated by

vagal activity (HF: .– Hz). A third component needs to

be considered: we called it Movement Frequency (MF: .–

. Hz). MF should quantify the activity of the fetus and the

mechanical inuences of the maternal breathing.

is approach works well on a short time scale (– min,

points about) as the stationarity of the fetal HRV signal

is an essential requirement. We adopted the autoregressive

power spectrum estimation method as described in Signorini

et al. [].

LF/HF + MF ratio could represent a synthetic index of

the balance between physiological control components and

fetus activity level, representing the equivalent of the so-

called sympathovagal balance in standard HRV analysis.

2.3. Nonstandard Parameters for FHR Analysis. e intro-

duction of nonlinear approaches to signal processing led to

considering a set of methods investigating geometric and

dynamic properties of time series.

Dierently from the approach usually adopted to study

a well-known deterministic system, when we deal with

complex nonlinear systems, very oen we can only analyze

experimental time series. Nevertheless important indications

can be extracted from the parameters estimating nonlinear

characteristics. eir statistical use can be of great impor-

tance, even in diagnostic eld and in clinical knowledge

related to dierent cardiovascular pathologies [].

Various techniques exist aimed at quantifying the degree

of similarity and/or complexity in time series which can be

computed directly on the sequence of interbeat intervals [,

].

2.3.1. Regularity Properties: Entropy Estimators (ApEn, Sam-

pEn). ApEn index quanties regularity and complexity of

a time series. e index was proposed in []andfurther

improvements and corrections were proposed by the intro-

duction of the SampEn index.

e idea is to quantify the degree of regularity or loss of

regularity in a time series without a priori information on

its structure. ApEn works on short (< samples) and noisy

time series.

ApEn estimator depends on a parameter (length of

runs compared in the time series) and on a parameter (per-

centage of signal std., working as a lter). e ApEn(,,)

evaluates, within a tolerance , the signal regularity, by

assessing the frequency of patterns similar to a given pattern

of window length (=1,2, : 0.1 − 0.25 std of the input

data []).

Once values of the two parameters and are xed

and given data points, the procedure constructs sequences

𝑚() and computes, for each ≤−+1,

𝑚

𝑖()=(−+1

)−1 number of ≤−+1

such that 𝑚(),𝑚 ≤ .

()

Regularity parameter is dened as

ApEn(,,) = lim𝑁→∞[Φ𝑚−Φ𝑚+1],whereΦ𝑚() =

(−+1)

−1 ∑𝑁−𝑚+1

𝑖=1 ln 𝑚

𝑖().

e estimator of this parameter for an experimental time

series of a xed length is given by ApEn(,,)=[Φ𝑚−

Φ𝑚+1].

Other methods estimate entropy-like indexes in time

series. Among them, Sample Entropy (SampEn) has been

largely employed in biomedical signal processing over

time, as it improves the estimation performed by ApEn (i.e.,

Computational and Mathematical Methods in Medicine

removes the bias introduced by self-counts). SampEn is also

the basis for a multiscale approach: entropy parameters are

calculated at dierent scales in coarse-grained time series

[,].

ApEnandSampEnwereestimatedinthesametimeseries

by using the same parameter set: =1and = 0.1,=2

and = 0.15 and ..

2.3.2. Lempel Ziv Complexity. Lempel Ziv complexity (LZC)

was originally proposed in the information eld to assess

the complexity of data series []. Its measure is associated

with the number of dierent substrings and to the rate of

their recurrence. Namely, LZC reects the gradual increase

of new patterns along the given sequence. e measure of

complexity introduced by Lempel and Ziv assesses the so-

called algorithmic complexity, which is dened according to

Information eory as the minimum quantity of information

needed to dene a binary string. In case of random strings,

the algorithmic complexity is the length of the string itself. In

fact any compression eort will produce an information loss.

In order to estimate the LZC in a time series, it is necessary

to transform the signal (the FHR in or case) into symbolic

sequences.

Calculation of the Lempel Ziv complexity () needs

to dene an alphabet A, that is, the set of symbols which

compose the sequence (for a binary string, A is simply

{0,1}).

Suppose the number of symbols in the alphabet A is and

thelengthofsequenceis() = .eupperboundof() is

given by:

()<

1 − 𝑛log (),()

where 𝑛= 2(1 + log log())/log() []. When is large

enough ( → ∞),𝑛→0andwehavethat

lim

𝑛→∞()=()=

log𝛼().()

e quantity () is the asymptotic behaviour of () for

a random string. e normalized complexity is thus dened

as () = ()/().

In order to estimate the complexity measure for the HRV

time series, we have transformed the signals in symbolic

sequences. As a coding procedure we adopted both a binary

and a ternary code. From an HRV series {𝑛},weconstructa

new sequence by mapping the original one through a binary

alphabet. We symbolize with a signal increase (𝑛+1 >

𝑛),andwithadecrease(𝑛+1 ≤

𝑛).Incaseofternary

alphabet, denotes the signal increase (𝑛+1 >

𝑛),the

decrease (𝑛+1 <

𝑛)andthesignalinvariance(𝑛+1 =

𝑛). To avoid the possible dependence of the encoded string

on quantization procedure adopted to record the signal, a

factor is introduced representing the minimum quantization

level for a symbol change in the coded string. We considered

the encoding parameter =0, ., ., .%. e

LZC index was computed point-long FHR sequences

( min).

50 100 150 200 250

Number of samples

300 350 400

1.5

1

0.5

0

Amplitude (bpm)

0

−0.5

−1

−1.5

−2

F : Phase Rectied Signal Average (PRSA) curve computed

on a FHR recording. e computation of the Acceleration Phase

Rectied Slope is shown: APRS is dened as the slope of the PRSA

curve in the anchor point (red dot).

2.3.3. Phase Rectied Signal Average (PRSA). Phase rectied

signal average (PRSA) is a technique introduced by Bauer et

al. in []. It allows the detection and quantication of

quasiperiodic oscillations in nonstationary signals aected by

noise and artifacts, by synchronizing the phase of all periodic

components. is method demonstrated its usefulness in

FHR signal analysis, when episodes of increasing and/or

decreasing FHR appear []. In fact, occurrence or absence of

such periods can be related to the healthy status of the fetus.

For this reason, we introduced the PRSA method to quantify

fetalwell-beingstates.

e PRSA curve is obtained from the HRV series. e

procedure that can be followed to construct the curve is

detailed and described in []. e great advantage given

by the PRSA curve is the fact that a –-minute HRV

signal can be condensed in a single waveform, showing the

average dynamic pattern of the recording under analysis. An

example of PRSA curve is shown in Figure ,wherethered

dot represents the anchor point and the dashed red line is the

slopeofthecurveintheanchorpoint.

In order to construct the curve, we employed sec

windows (total number of samples) obtained from the

FHR signal, which were selected if the right average of the

window was bigger than the le average. en, the windows

were synchronized in their anchor point (the middle point of

the curve) and averaged.

Starting from the PRSA curve, it is possible to compute

several parameters that describe its shape and, indirectly,

quantify the overall dynamics in the HRV series. us, those

parameterscanbeemployedtoprovideaclueaboutfetal

behavior and well-being.

In [], we proposed the Acceleration Phase Rectied

Slope (APRS) and the Deceleration Phase Rectied Slope

(DPRS), as useful indices computed on the PRSA curve in

order to verify fetal well-being. For a detailed description of

how these parameters are computed, please refer to [].

Table summarizes all the parameters we have con-

sidered in fetal HR analysis. Parameters have been grouped

as Frequency domain (autoregressive power spectrum

Computational and Mathematical Methods in Medicine

T : Methods, extracted parameters, sequence lengths, and hypotheses for using the relevant parameter.

Method Parameters Sequence length Hypothesis

Frequency domain analysis:

periodogram and

autoregressive model

Measurement of spectral

components in dened

frequency bands

%ofspectralpower(msec

)infrequencybands:

Low frequency .–. Hz

Movement (activity) frequency .–. Hz

High frequency .– Hz

LF/(MF + HF)

min

values

Quantication of the

activity of the autonomic

nervous system

Time domain analysis:

morphological HR

modication and variability

STV (msec)

II

min

values

Variabi l i t y i n t h e s h o r t

period

FHR avg (msec)

LTI ( mse c)

min

values

Variabi l i t y i n t h e l o n g

period

Approximate entropy ApEn (,)=,;= ., ., . min

= 360 values Recurrent patterns

Sample entropy SampEn(,)=,;= ., ., . min

= 360 values

Recurrent patterns

Basis for investigating

repetitive patterns at

dierent time scales

Lempel Ziv complexity

(LZC)

LZC binary or ternary coding

LZC ( or , = , ., , , .) Whole recording

Rate of new patterns arising

with signal evolution in

time

PRSA Acceleration/Deceleration Phase Rectied Slope Whole recording Quasiperiodic oscillations

estimation—LF-power, MF-power, HF power, and LF/(MF

+ HF)); time domain (short term variability (STV), long

term irregularity (LTI), Interval Index (II)); and regularity

and complexity parameters (approximate entropy (ApEn),

sample entropy (SampEn), Lempel Ziv complexity (LZC),and

nally PRSA parameters). All parameters are listed in Ta bl e

according to the time windows, which are suggested on the

basis of our results.

For each group of them the pathophysiological meaning

or the most reliable hypothesis is presented.

By this approach to the study of FHR we performed

classication of dierent fetal states and we obtained diag-

nostic indications in pathologies such as intrauterine growth

restriction (IUGR) and fetal distress [,].

3. Results

Results are reported for the two groups of fetuses concerning

theparametersillustratedinSections:amongthetimeparam-

eters, STV, II, and LTI were selected; all frequency domains

indices were computed by using the autoregressive power

estimation (LF, MF, HF, and the ratio LF/(HF + MF)); among

non-linear parameters, ApEn and SampEn were selected and

compared to quantify non-linear complexity characteristics

of FHR series; LZC parameters add information about com-

plexity and predictability of FHR time series; nally, for the

PRSA based parameters, APRS and DPRS were considered.

e target of the study was to identify which parameter or

parameter set is most ecient in the discrimination between

healthy and IUGR fetuses. Analysis of the FHR that consider

more than one parameter at time has the objective to early

identify signs of fetal distress that could bring interventions

against possible life-threatening events.

In order to verify the ability of the selected parameters

to discriminate between healthy and IUGR fetuses, we rst

veried that the two populations showed Gaussian distribu-

tions for all parameters using the Kolmogorov-Smirnov test,

in order to further apply the -test for the discrimination.

Table summarizes the results concerning the healthy

and IUGR groups of fetuses. Among the time parameters,

both STV and LTI show great performance in the discrimi-

nation task (STV: -value = 1.22−9;LTI:value=1.5−11),

while Interval Index does not.

Results in frequency domain parameters show a weak

capability to dierentiate normal versus IUGR fetuses. Nev-

ertheless, many results reported in the literature demonstrate

their ability in assessing the cardiovascular well-being in

adults. So they still remain important candidates to monitor

cardiovascular regulation dynamics in FHR time series,

although in this case they do not seem able to discriminate

IUGR fetuses. As a matter of fact, the frequency parameters

are related to physiological mechanisms acting on the heart

control. So, measuring the HF component of the PSD is

a way to measure respiratory fetal activity providing a

parameter directly related to hypoxia or to a respiratory stress

state.

e analysis of non-linear parameters shows that all

considered parameters allow the rejection of the null hypoth-

esis: ApEn(1,0.1) with -value 5.14 − 07,conrmingtobe

highly sensitive to the IUGR condition, LZC(2,0) with -

value 7.8 − 4,andSampEn(1,0.1) with -value 2.08 − 7,

demonstrating a very high discriminant ability between the

two groups.

Moreover,evensimilaranalysiswedidinadierent

population of normal and IUGR fetuses by using multiscale

entropy approach []alsoprovidedsatisfyinglevelsof

discrimination power of the entropy indices, thus conrming

Computational and Mathematical Methods in Medicine

T : Results of fetal HRV analysis by parameters in time domain, in frequency domain, by nonlinear indices and PRSA derived indices.

Usefulness in separating populations is conrmed by -test results.

Parameter Healthy IUGR -test value

(mean ±std) (mean ±std)

Time parameters

STV (ms) . ±. . ±. ∗ ∗ ∗ 1.22 − 09

Interval index . ±. . ±. .

LTI ( ms) . ±. . ±. ∗ ∗ ∗ 1.5 − 11

Frequency domain

LF (Low Frequency power) . ±. . ±. .

MF (Movement Frequency power) . ±. . ±. .

HF (High Frequency power) . ±. . ±. .

LF/HF + MF . ±. . ±. .

Nonlinear parameters

ApEn(, .) . ±. . ±. ∗∗ 5.14 − 7

Lempel Ziv complexity(, ) . ±. . ±. ∗.

SampEn(, .) . ±. . ±. ∗∗ 2.08 − 7

PRSA parameters

APRS . ±. . ±. ∗ ∗ ∗ 7.76 − 12

DPRS −. ±. −. ±. ∗ ∗ ∗ 1.08 − 13

the diagnostic and clinical usefulness of this family of

parameters.

Among PRSA parameters, both APRS and DPRS, were

demonstrated to be highly selective for the separation of

the two groups. e APRS allows the rejection of the null

hypothesis with a -value of 7.76 − 12.eDPRSbehaves

even better, with a -value of 1.08 − 13.eDPRSisthe

parameter in the analyses which exhibits the smallest -value

in the discrimination between healthy and IUGR patients. On

the contrary other PRSA parameters reported in the literature

by Huhn et al. [], when applied to our population of fetuses,

are not ecient in the discrimination as already reported in

[].

Figure shows the boxplots of the subset of parameters

which show signicant -values ( < 0.05) computed in the

analysis of the two groups of fetuses.

A further improvement of the diagnostic ability of our set

of parameters could be obtained by a multivariate analysis, in

which two or more parameters are considered together for the

discrimination task. We did not perform a multiparametric

analysis in depth for the many combinations of indices we

computed, but we can support the previous claim by some

preliminary results. Figure shows as an example of what can

be obtained by combining the discrimination power of two

parameters: plot of ApEn(1,0.1) versus LTI values shows how

healthy and IUGR populations can be separated, with very

few errors, in dierent subspaces.

4. The Future: Wearable Technology for

Fetal Monitoring

Monitoring fetal states can also be performed by measuring

fetal ECG through electrodes placed over the maternal

abdomen aer the th week of pregnancy [], which

directlyprovideameasureoftheFECG.Unfortunately,it

isverydiculttoreliablyrevealthisFECGbothforthe

low SNR, due to noise superimposed and maternal ECG

interference, and for the position of the fetus that almost

continuously changes his position inside the uterus. e

recording can be made only at the hospital and requires the

presence of expert personnel. Even in that case, measurement

of FECG remains a dicult task.

Nevertheless,recordingtheFECGcouldprovideinfor-

mation on the beat structure (long QT, T wave morphology

and slope), which is related to heart diseases and to hypoxic

fetal states. Moreover, FECG recordings allow longer periods

of HRV measurements with respect to CTG which employs

ultrasounds(beingtheECGcompletelynoninvasive).e

idea is to design a “Fetal Holter” for very long FHRV signal

acquisitions.

With this focus, recent evolution in wearable technol-

ogy has started to produce eects even in the biomedi-

cal devices eld. As a matter of fact, these new wearable

devices allow measuring several physiological parameters

continuously in normal life conditions for long periods.

us, interesting perspectives are now open toward the

development of new systems, even in the eld of fetal

monitoring.Withthisfocus,ourresearchgrouphasdesigned

a new monitoring system, namely, the Telefetalcare sys-

tem, that makes use of wearable technologies to measure

FECG [] through textile electrodes embedded in everyday

garments.

A rst example of what we can obtain by a wearable pre-

natal garment sensorized with ECG textile electrodes and

a miniaturized acquisition system is illustrated in Figure ,

where one lead of the fetal-maternal ECG is reported together

with the QRS detection. Till now, the Telefetalcare has

been used on a limited number of patients, showing good

performances in both terms of quality of the acquired signals

Computational and Mathematical Methods in Medicine

0

2

4

6

8

10

12

STV (ms)

IUGRs Healthy IUGRs Healthy

LTI (ms ∗10)

(a)

0.6

0.8

1

1.2

1.4

1.6

1.8

2

IUGRs Healthy IUGRs Healthy IUGRs Healthy

LZC(2, 0) ApEn(1, 0.1) SampEn(1, 0.1)

(b)

0

0.5

1

1.5

2

2.5

3

3.5

IUGRs Healthy IUGRs Healthy

|DPRS|

Min

Median

Max

APRS ∗10

∗10

(c)

F : Boxplots of the signicant parameters (the height of each box represents the distance between quartile (%) and quartile (%));

the triangular marker is the median; x denotes the maximum; and - marker is the minimum. (a) Diagram contains time domain indices, (b)

diagram non linear indices and (c) diagram PRSA indices.

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

5 1015202530354045

LTI (ms)

Healthy

IUGRs

ApEn(1, 0.1)

F : Indiv idual data of ApEn(1,0.1) versus LTI. e two groups

of IUGRs and healthy fetuses occupy dierent subspaces in the

diagram and can be separated quite easily with very few errors.

and in terms of fetal QRSs detection. At the moment both the

separation of fetal-maternal ECGs and the digital processing

areperformedoineonanotebookcomputer,usinga

graphical user interface implemented in Matlab environment.

e nal goal of this novel approach is to produce a

system that every pregnant woman can use at home, able to

collect FECG signal, for long periods, in a comfortable way,

andtosenddatatothehospitalforevaluation,througha

wireless link.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Time (s)

F : Example of ECG recording taken from the Telefetalcare

system. e identication of maternal (gray dots, down) and fetal

(red dots, up) heart beats is computed o-line by a novel algorithm

implemented in Matlab.

Figure illustrates the functional architecture of the

whole system. Acquisition of the cardiac electric signals

takes place through a dedicated hardware device which is

wireless connected to the patient through the sensorized

garment. To reduce the costs connected with the hardware

manufacturing, the device has no display for user interface

and only consists of an -channel dierential amplier,

paired with a BluetoothTM wireless communication module.

Smartphones or tablets available nowadays are endowed

with high resolution color screens whose capabilities outpace

Computational and Mathematical Methods in Medicine

Patient Cardiac signal

acquisition device

Wireless bluetooth

connection Smartphone

UMTS connectionClinic server

Browser station

F : Actual architecture of the Telefetalcare system.

those of any other rendering device and computers available

in the past decade.

Our objective is to obtain a high quality fetal ECG signal,

for long periods, in an unsupervised environment (mother

normal life) to extract fetal HRV in order to use it as an

indicator of fetal well-being and/or stress conditions.

Of course, the analysis methods, previously presented and

adopted for the fetal HRV signal from CTG recordings, will

be used in the system postprocessing step. As a matter of fact,

a signicant improvement in the quality of fetal well-being

assessment could be obtained by more frequent and accurate

signal measurements and analysis, as costs in fetal monitoring

will be drastically reduced.

5. Discussion

e paper presents results obtained from the application of

several analysis tools to fetal heart rate variability signals.

FHR signals were recorded through CTG in normal and

IUGR fetuses, with the goal of demonstrating that fetal mon-

itoring can be strongly improved by new analysis techniques

and parameters related to pathophysiological fetal states.

e work evidenced some important points.

First, FHRV signal carries a lot of information about

fetal condition during pregnancy and CTG, being the most

employed technique supporting the diagnostic process along

the nal part of the pregnancy, and allows extracting this

information through an accurate analysis. We considered a

population including normal and IUGR subjects and we

checked dierent approaches to nd out reliable indices for

separatingthetwogroups.Wetestedtimedomain,frequency

domain, and nonlinear approaches and results showed that

time domain and nonlinear indices signicantly separate

the two groups allowing a clear classication. is is very

important as early identication of IUGR condition allows

proper intervention reducing life-threatening events.

However, not all parameters are equally sensitive to

evolving fetal conditions. Entropy parameters, Lempel Ziv

complexity indices, variability parameters in time domain,

and PRSA derived indices exhibit excellent performance in

classication of normal and IUGR population. Nevertheless

it is necessary to stress the importance of considering a

quite large set of parameters to investigate the complex

regulation of the fetal cardiovascular system. e interaction

with the placenta, thus with the mother circulation, and the

development of the controlling systems in the fetus are all

factors inuencing and acting on the fetal state.

Results and examples shown in the paper clearly sug-

gest that monitoring systems could be improved by adding

diagnostic and classication power through advanced signal

processing techniques.

In particular, we want to stress the importance of

adopting a multiparameter analysis to better identify fetal

states for the sake of preventing disease insurgence. Our

preliminary analysis (ApEn/LTI in Figure ) shows how the

simple combination of two parameters can improves the

identication of IUGR subjects from healthy ones. ese

aspects deserve future investigations through a multivariate

analysis.

Another important point relies on the general use the

proposedapproachcouldhaveinthefetalHRanalysisas

CTG data are routinely measured during pregnancy. As a

matter of fact, analysis tools can complement the clinical

routine steps, providing further indications to physicians and

nurses.

Our experience has shown that implementing advanced

signal processing techniques can provide better classication

resultsofthefetalstateseitherinanormaldevelopmentofthe

pregnancy (activity-quiet) [] (vibroacoustic stimulation)

[] or in pathological conditions (distressed fetuses) []

(IUGRs) [,].

Moreover, the intrinsic complexity which characterizes

fetal life and the possible associated diseases complicates the

prediction and control of fetal development. To face this

problem we need to develop more personalized monitoring

system allowing an almost continuous noninvasive evalua-

tion of the fetal state and in which knowledge based systems

contribute to the care improvement.

Asafurthercontributiontoaknowledgebasedfetal

monitoring approach, supported by an advanced technology,

we have briey presented a fetal ECG monitoring system,

Telefetalcare, based on wearable technology and designed to

permit an accurate and continuing assessment of fetal well-

being. Advantages are in the signal quality with the direct

measurement of fetal HRV and the long-term monitoring

that can be easily performed. A wearable garment equipped

with textile electrodes will allow pregnant women to monitor

fetus health state without moving to the hospital, always

having the clinician remote support.

e system can contribute to reducing costs of fetal

monitoring still maintaining a signicant quality or even

improving the fetal wellbeing assessment.

ese novel approaches can open a new window on

the continuous monitoring of fetal development: further

information can be extracted by introducing novel analysis

tools, more sensitive to fetal states both in healthy and stress

conditions, by increasing length, frequency, and quality of

monitoring session. Methods and technological advance-

mentsbothhaveakeyrolecontributingtoreachingthis

important scientic and social objective.

Computational and Mathematical Methods in Medicine

Conflict of Interests

e authors declare that there is no conict of interests

regarding the publication of this paper.

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