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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 classification and extraction of useful diagnostic information. The paper is focused on monitoring procedures applied to fetal heart rate variability (FHRV) signals, collected during pregnancy, in order to assess fetal well-being. The 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. The paper shows how advanced signal processing approaches can contribute to developing new diagnostic and classification indices. Their usefulness is evaluated by comparing two selected populations: normal fetuses and intra uterine growth restricted (IUGR) fetuses. Results show that the computation of different 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 briefly describes how the introduction of wearable systems for fetal ECG recording could provide new technological solutions improving the quality and usability of prenatal monitoring.
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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 classication 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 classication 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 dierent
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 briey 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-
tication, evaluation, and classication 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 dierent nature. eir
interaction produces changes in the beat rate assuring the
system controlling heartbeats reacts eciently to dierent
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 quantication 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 aecting HRV
signal, both in short and long temporal windows, nonlinear
signal analysis has demonstrated its usefulness [].
Intheeldoffetalheartratemonitoringduringpreg-
nancy, linear time and frequency techniques were tradition-
ally adopted. Fetal HR monitoring is a challenging procedure
forpeopleworkingintheobstetriceld,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 briey
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 soware then determines the heart
period (the equivalent of RR period) from the autocorrelation
function. With a peak position interpolation algorithm, the
eective 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
oer 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 modied the soware 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 dened “a
posteriori,” aer delivery, on the basis of standard parameters
(Apgar scores, weight, abdominal circumference): IUGR
Computational and Mathematical Methods in Medicine
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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 aer
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 dened as deviations from the baseline,
and more than one quantitative denition 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 sucient 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, dierent quantications of the words “deviations”
and “sucient” 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 denition 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, aer
the removal of the undesired parts, must contain, at least, a
continuous segment of  seconds.
Given a signal 24() with ∈[;], LTI is dened 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)
quanties FHR variability over a very short time scale, usually
on a beat-to-beat basis. We refer to the denitions 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 dened 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 aer 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 inuences 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.
Dierently from the approach usually adopted to study
a well-known deterministic system, when we deal with
complex nonlinear systems, very oen 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 dierent 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 quanties 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 dened as
ApEn(,,) = lim𝑁→𝑚−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 dierent 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 dierent substrings and to the rate of
their recurrence. Namely, LZC reects 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 dened according to
Information eory as the minimum quantity of information
needed to dene a binary string. In case of random strings,
the algorithmic complexity is the length of the string itself. In
fact any compression eort 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 dene 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 dened
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),andwithadecrease(𝑛+1 ≤
𝑛).Incaseofternary
alphabet,  denotes the signal increase (𝑛+1 >
𝑛),the
decrease (𝑛+1 <
𝑛)andthesignalinvariance(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 Rectied Signal Average (PRSA) curve computed
on a FHR recording. e computation of the Acceleration Phase
Rectied Slope is shown: APRS is dened as the slope of the PRSA
curve in the anchor point (red dot).
2.3.3. Phase Rectied Signal Average (PRSA). Phase rectied
signal average (PRSA) is a technique introduced by Bauer et
al. in  []. It allows the detection and quantication of
quasiperiodic oscillations in nonstationary signals aected 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 Rectied
Slope (APRS) and the Deceleration Phase Rectied 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 dened
frequency bands
%ofspectralpower(msec
)infrequencybands:
Low frequency .–. Hz
Movement (activity) frequency .–. Hz
High frequency .– Hz
LF/(MF + HF)
 min
 values
Quantication of the
activity of the autonomic
nervous system
Time domain analysis:
morphological HR
modication 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
dierent 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 Rectied 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
classication of dierent 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 ecient 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
veried 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=1.511),
while Interval Index does not.
Results in frequency domain parameters show a weak
capability to dierentiate 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 conrming
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 conrmed 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 ecient in the discrimination as already reported in
[].
Figure  shows the boxplots of the subset of parameters
which show signicant -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 dierent 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 aer 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 dicult 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 eects 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 signicant 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 dierent 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 identication 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 dierential amplier,
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 signicant 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 dierent approaches to nd out reliable indices for
separatingthetwogroups.Wetestedtimedomain,frequency
domain, and nonlinear approaches and results showed that
time domain and nonlinear indices signicantly separate
the two groups allowing a clear classication. is is very
important as early identication 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
classication 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 inuencing 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 classication 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
identication 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 classication
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 briey 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 signicant 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 scientic and social objective.
 Computational and Mathematical Methods in Medicine
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
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... The linear modeling approaches quantify sympathetic and parasympathetic control mechanisms and their balance by measuring spectral low and high-frequency components. However, it has been shown that not all information carried by beat-to-beat variability can be explained by these components (27). For this matter, in the past couple of decades, and with the fast development of computation, new signal processing, and pattern recognition methodologies have been developed and applied to many different fields, including the analysis of fHRV using non-linear parameters (28,29). ...
... They range from simple feature extraction methods to more sophisticated classification programs and joining research centers from different countries for joint projects, as the Digi-Newb project (244). Usage of continuous non-invasive evaluation, such as the usage of wearables, have been discussed (27,245) and will contribute to the patient's care improvement since it will improve data gathering, reducing costs of fetal monitoring. Insurgent approaches are opening new windows on the continuous monitoring of fetal development. ...
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The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.
... By assessing the linear time and frequency techniques, S. Boudet et.al., [14] used different matlab comments and 12 re-coded literature methods for fetal heart rate analysis. Maria G.Signorini et.al., [15] proposed the work in Fetal Heart Rate Variability signals collected during pregnancy. ...
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Cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. CTG also used for measuring heart rate of the patients along with fetal's heart rate. CTG classification can be done using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal stress monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. The main aim of this work is to develop a flexible and an effective system for monitoring the fetal stress, which leads to a decrease in computing time with increasing speed. Traditional methods of classification using machine learning algorithmic techniques are not able to take more attributes for selecting the feature. The dataset used in the proposed work are having many attributes for feature selection. So this work will adopt two techniques such as, KNN Classification Algorithm & FHR parameter estimation based classification for classifying the signal as Normal, Suspicious and Pathological. The efficiency of proposed work was analysed and compared with existing methods. The results show that the proposed work provides better results than other methods.
... This reflects what has already been shown in adult subjects by Voss et al. [130,157], who suggested that the combination of different HRV parameters from both time and frequency domains as well as from nonlinear dynamics can improve diagnostic precision in the analysis of HRV. Moreover, this statement is also in agreement with further studies on FHRV, which showed that the choice of the optimal linear/nonlinear parameter combination could improve the classification between healthy and IUGR fetuses [158] and, in general, could be more effective in classifying FHR patterns and assessing ANS maturation [64,159,160]. ...
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The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.
... A representative example is the fetal heart rate (FHR) measurement, which is regarded as one of the most important approaches used for monitoring the cardiac status of fetal human beings. 7 In the past, various applications have been proposed for the FHR measurements, e.g., see the work of Ibrahimy et al. [8][9][10][11][12][13][14] and references therein. It should be emphasized, however, that the cost of the majority of these applications is relatively high, i.e., over 300 dollars, and thus motivates the use of more economical solutions. ...
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Currently, hospitals and health care sectors employ low-cost Internet of Things based remote health monitoring systems and labs in order to collect a subject's or patients’ real-time data. Such a process can be helpful for the early detection of a healthy newborn life and of critical importance for the survival of these lives. In this article, a preliminary implementation of a system monitoring the fetus heart rate (FHR) has been designed and implemented as a mobile wearable measuring system with remote sensing. The proposed implementation turns out to be an efficient combination of simplicity and cost effectiveness and is accompanied with preliminary accurate measurements of the FHR. The proposed system uses a transceiver module and is capable of efficient data transmission to a remote server station using a IEEE 802.11 b/g/n based wireless network. The patients’ data can further be monitored using a smart or satellite phone, or even any well-known internet browser connected to the specific network, thus complying with the health safety distance measures required due to various situations, including that of the COVID-19 pandemic.
... Research also extended to fetal hiccups [5], [6]. Meanwhile, the issues of Sudden Infant Death Syndrome (SIDS) [7] and Intrauterine Growth Retardation (IUGR) inspired further studies investigating fetal activity [8], [9]. FBMs also play an important role in the development of the respiratory center in the brainstem [10]. ...
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... They identified several miRNAs (miR-16-5p, miR-100-5p, miR-122-5p, miR-125b-5p, miR-126-3p, miR-143-3p, miR-195-5p, miR-199a-5p, miR-221-3p, miR-342-3p, and miR-574-3p) which were down-regulated in FGRs (36). The plasma levels of several others hypoxia-induced miRNA have been recently associated with FGR (25,37). In the last decade contrasting results have been reported on the miRNA expression levels in circulating blood and in placenta tissue. ...
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Current tests available to diagnose fetal hypoxia in-utero lack sensitivity thus failing to identify many fetuses at risk. Emerging evidence suggests that microRNAs derived from the placenta circulate in the maternal blood during pregnancy and may be used as non-invasive biomarkers for pregnancy complications. With the intent to identify putative markers of fetal growth restriction (FGR) and new therapeutic druggable targets, we examined, in maternal blood samples, the expression of a group of microRNAs, known to be regulated by hypoxia. The expression of microRNAs was evaluated in maternal plasma samples collected from (1) women carrying a preterm FGR fetus (FGR group) or (2) women with an appropriately grown fetus matched at the same gestational age (Control group). To discriminate between early- and late-onset FGR, the study population was divided into two subgroups according to the gestational age at delivery. Four microRNAs were identified as possible candidates for the diagnosis of FGR: miR-16-5p, miR-103-3p, miR-107-3p, and miR-27b-3p. All four selected miRNAs, measured by RT-PCR, resulted upregulated in FGR blood samples before the 32nd week of gestation. By contrast, miRNA103-3p and miRNA107-3p, analyzed between the 32nd and 37th week of gestation, showed lower expression in the FGR group compared to aged matched controls. Our results showed that measurement of miRNAs in maternal blood may form the basis for a future diagnostic test to determine the degree of fetal hypoxia in FGR, thus allowing the start of appropriate therapeutic interventions to alleviate the burden of this disease.
... Consequently, there have been attempts to move away from pattern recognition to physiological approach at CTG interpretation, possibly trying to reduce the variations. 7 It is more likely that the use of additional high-tech advances, such as remote wearable technology 8 and computer-analyzed technologies 9 and artificial intelligence, may likely replace the human interphase or interpretation for investigations, such as CTG or EFM. However, not having a defined or common agreement is likely to compromise this future perspective for any progress in EFM. ...
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Objective Electronic fetal monitoring (EFM) or fetal assessment by a cardiotocograph (CTG) is currently the most commonly employed tool for intrapartum surveillance. There are numerous guidelines that inform best practice across the world. The systematic review aims to compare and appraise all the available practice guidelines on intrapartum EFM to describe the similarities and variations in recommendations. Study design A systematic protocol was developed as per PRISMA-P (Preferred Reporting Item for Systematic Review and Meta-Analysis). Four independent reviewers were involved with independent searches and quality assessment using the appraisal of guidelines for research and evaluation (AGREE II) instrument for guideline quality reporting. Results Seven international practice guidelines are included in this systematic review. AGREE II showed higher scores for scope & purpose and for clarity of presentation, however the overall assessment varied between 25% and 89%. When individual characteristics of EFM/CTG are compared, all guidelines and guidance are essentially trying to describe the characters similarly, with some very important differences as described in the full article. Conclusion In the context of globalization, a uniform approach for defining terminology, classifying characters and similar interpretation of results is needed for electronic fetal monitoring. We should therefore consider a unified, simple, logistically approved and acceptable guideline, which is likely to be accepted across the world.
... The clinical assessment of the foetal heart activity is an important step for diagnosis 1,2 , and monitoring purposes 3,4 . Different instrumental techniques can be adopted depending on the gestational age and goal of the examination, including ultrasonography (cardiotocography and echocardiography, mainly in the B-mode and M-mode 5 and Doppler [6][7][8][9] ), phonocardiography 10,11 , magnetocardiography [12][13][14] , invasive electrocardiography (ECG) 15 , and non-invasive foetal ECG (fECG) [16][17][18] . ...
Article
Full-text available
Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21 st and 27 th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.
Conference Paper
Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal.
Article
Objectives: This work aims at characterizing the variation of fetal heart rate (FHR) provoked by vibro-acoustic stimulation (VAS). The FHR signal is analyzed by means of a multiparametric approach consisting of linear and nonlinear indices. Methods: The FHR signals of 13 fetuses were collected through a US standard CTG monitor (HP1351A) and were sampled at a frequency of 2 Hz. The VAS was provided after a period of quiet of 10 minutes. The analysis was performed on the quiet period and on two successive time windows of 10 minutes each, after the stimulation. FHR classical parameters (delta, short term variability, long term irregularity, accelerations and decelerations) as well as power spectral density (PSD) and approximate entropy (ApEn) were computed for each period. Results: Results confirm that there is a significant change in fetal conditions after the stimulus is applied. This change can be clearly observed either in time domain parameters and in the regularity index (ApEn). Individual data are all consistent with an increase of variability and a decrease of regularity after VAS. Conclusions: The obtained results give further strength to the hypothesis that vibratory stimuli tests represent a reliable method for monitoring the neural development of the fetus during pregnancy.
Book
This book surveys recent developments in the analysis of physiological time series. The authors, physicists and mathematicans, physiologists and medical researchers, have succeeded in presenting a review of the new field of nonlinear data analysis as needed for more refined computer-aided diagnostics. Together with the techniques, they actually propose a new approach to the problems. The practitioners may find the many applications to the cardio-respiratory system, EEG analysis, motor control and voice signals very useful.
Article
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