ArticlePDF Available

Variable-Length Accelerometer Features and Electromyography to Improve Accuracy of Fetal Kicks Detection During Pregnancy Using a Single Wearable Device

Authors:

Abstract

In this paper, we propose a method to improve accuracy of fetal kicks detection during pregnancy using a single wearable device placed on the abdomen. Monitoring fetal wellbeing is key in modern obstetrics as it is routinely used as a proxy to fetal movement. However, accurate, nonin-vasive, long-term monitoring of fetal movement is challenging, especially outside hospital environments. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote, self-administrated monitoring of fetal movement. These solutions typically consist in multi-accelerometer systems of limited practical applicability, relying on simple features such as the signal magnitude. In this paper, we propose two methods to improve accuracy of fetal kicks detection using a single wearable device, in particular aiming at reducing false positives and increasing positive predictive value (PPV) when lacking a reference accelerometer outside the abdominal area. Firstly, we propose variable-length accelerometer features. Secondly, we combine accelerometer data with electromyography (EMG). Both the proposed techniques aim at providing more contextual information related to maternal movement while still using a single wearable device. We compare our method to a system comprising 6 accelerometer sensors over a dataset including 22 recordings and reference maternal annotations, highlighting how kicks detection PPV can be improved by up to 10% when including variable-length features and up to 11% when including EMG features.
Variable-Length Accelerometer Features and Electromyography to
Improve Accuracy of Fetal Kicks Detection During Pregnancy Using a
Single Wearable Device
Marco Altini1, Elisa Rossetti2, Michiel Rooijakkers2, Julien Penders1,
Dorien Lanssens3, Lars Grieten3and Wilfried Gyselaers3
Abstract In this paper, we propose a method to improve
accuracy of fetal kicks detection during pregnancy using a
single wearable device placed on the abdomen. Monitoring
fetal wellbeing is key in modern obstetrics as it is routinely
used as a proxy to fetal movement. However, accurate, nonin-
vasive, long-term monitoring of fetal movement is challenging,
especially outside hospital environments. A few accelerometer-
based systems have been developed in the past few years, to
tackle common issues in ultrasound measurement and enable
remote, self-administrated monitoring of fetal movement. These
solutions typically consist in multi-accelerometer systems of
limited practical applicability, relying on simple features such
as the signal magnitude. In this paper, we propose two methods
to improve accuracy of fetal kicks detection using a single
wearable device, in particular aiming at reducing false positives
and increasing positive predictive value (PPV) when lacking a
reference accelerometer outside the abdominal area. Firstly,
we propose variable-length accelerometer features. Secondly,
we combine accelerometer data with electromyography (EMG).
Both the proposed techniques aim at providing more contextual
information related to maternal movement while still using a
single wearable device. We compare our method to a system
comprising 6accelerometer sensors over a dataset including
22 recordings and reference maternal annotations, highlighting
how kicks detection PPV can be improved by up to 10%
when including variable-length features and up to 11% when
including EMG features.
I. INTRODUCTION AND RELATED WORKS
Monitoring fetal movement during pregnancy is the most
practical and widespread method to assess fetal wellbeing,
one of the most important and complex tasks of modern
obstetrics. As birth outcomes are strongly linked to the
development of fetal conditions during pregnancy [1], several
techniques have been developed to monitor fetal movement
up to date [2].
Some methods require hospital stays or trained personnel,
for example ultrasound, relying on high frequency sound
waves being used to generate an image of the fetus and
can be used only for a limited amount of time due to
safety concerns [3], [4]. Other methods, such as continu-
ous cardiotocography, require cumbersome infrastructure and
This work was funded by Bloomlife
1M. Altini and J. Penders are with Bloomlife, San Francisco, USA
altini.marco@gmail.com
2M. Rooijakkers and E. Rossetti are with Bloom Technologies, Genk,
BE
3D. Lanssens, L. Grieten and W. Gyselaers are with the Department of
Future Health, Ziekenhuis Oost-Limburg, Genk, BE
hospital visits, also involving trained personnel to set up the
device and process the produced information [5], [6].
Thus the inability of these methods to monitor fetal
movement outside of sporadic spot checks in the hospital
environment is one of the major causes of concern and
motivations behind the development of other passive methods
for home-monitoring, such as accelerometer based solutions.
Wearable devices including on-board accelerometers pro-
vide new opportunities to investigate passively and safely
fetal movement inside [7] or outside [8] the hospital. Most
studies to date involved one single accelerometer placed on
the abdomen and reported rather low sensitivity and speci-
ficity [9]. Other researchers added a reference accelerometer
with the rationale that by monitoring maternal movement
artifacts using an accelerometer placed outside of the ab-
dominal area, fetal movement should be separable from
maternal movement and therefore detected more accurately
[8]. However, most solutions limit practical applicability as
additional sensors are placed far from the abdomen, given the
inability of additional accelerometers placed on the upper
thoracic area to discriminate between maternal and fetal
movement [10]. As a result, systems are often bulky and
cumbersome [11].
Recently, a few authors introduced machine learning tech-
niques to classify a set of features into a binary problem
(movement vs no-movement), obtaining promising results
[12], [13], [11], [14]. In the context of using supervised
learning methods to classify movements and non-movements,
an additional challenge arises. Fetal movement occurs only
for a small percentage of the time during a measurement,
therefore proper methods such as downsampling of the
majority class (i.e. no-movement) need to be employed [11].
In our previous work [11] we used Random Forests and
a set of time domain features computed over short windows
(0.5 seconds) and highlighted consistent improvements when
including a reference accelerometer on the back. A reference
accelerometer typically provides advantages in terms of
reducing false positives as maternal movement can be better
discriminated from fetal movement, as shown in Fig. 1.
In this paper, we hypothesized that employing variable-
length accelerometer features and sensor-fusion techniques
we could reduce false positives for prediction models derived
using a single wearable device. Our assumption is based
on exploiting the different patterns and time dynamics of
maternal and fetal movements.
0
100
200
0 5 10 15
Timestamp (minutes)
Motion intensity
label
nothing
kick
Multi − data sample accelerometer 1
0
50
100
150
200
0 5 10 15
Timestamp (minutes)
Motion intensity
Multi − data sample accelerometer 3
0
10
20
30
40
50
0 5 10 15
Timestamp (minutes)
Motion intensity
Reference accelerometer on the back − 6
0.0
0.1
0.2
0.3
0 5 10 15
Timestamp (minutes)
Motion intensity
label
nothing
kick
Single sensor, short time window
0.0
0.1
0.2
0 5 10 15
Timestamp (minutes)
Motion intensity
Single sensor, long time window
0
100
200
300
0 5 10 15
Timestamp (minutes)
EHG intensity
Single sensor, EHG data
Fig. 1. Left side plots: motion intensity (mean of the band passed
accelerometer signal summed over the three axes) for the TMSi device,
comprising 6accelerometers (only 3shown). Fetal kicks as manually
annotated by the subject are highlighted. We can clearly see spikes due
to maternal movement appearing on the reference accelerometer on the
back, number 6. On the other hand, fetal kicks are visible only on the
accelerometers on the abdomen, with similar accelerometer patterns across
sensor locations. Right side plots: motion intensity and EMG data for
the Bloomlife device, computed over variable-length time windows. Fetal
kicks are visible only for short time windows (0.5seconds) while maternal
movement artifacts are propagated on long time windows as well, hence we
hypothesized false positives could be reduced by including variable-length
features.
Thus, practical applicability and ease of use in home
settings could be improved without sacrificing accuracy.
The proposed techniques aim at reducing false positives by
providing more contextual information related to maternal
movement while still using a single wearable device to cope
with the absence of a reference accelerometer or a more
obtrusive system. We compare our method to a system com-
prising 6accelerometer sensors over a dataset including 22
recordings with reference maternal annotations, highlighting
how kicks detection PPV can be improved by up to 10%
when including variable-length features and up to 11% when
including EMG features.
II. DATA ACQUISITION
A. Accelerometers Data and Reference
Twenty-two recordings of about 20 minutes duration were
collected from 22 pregnant women at different gestational
ages during pregnancy, all from week 30 onwards. Fetal
movements per 20 minutes measurements were 34 ±68,
ranging between 0for inactive babies to 315 for hiccups
cases. Measurements were performed using two devices.
Firstly, we used a research version of the Bloomlife wearable
device [15], configured to acquire two channels EMG at
4096 Hz and triaxial accelerometer data at 128 Hz from a
Fig. 2. On-body accelerometers placement for the TMSi device: 5
accelerometers placed on the abdomen, while the sixth accelerometer,
placed on the back, is not visible. Bloomlife sensor placed below TMSi
accelerometer 1, capturing accelerometer and EMG data. Also visible are
TMSi electrodes used to acquire ExG data, not used in this study.
single accelerometer placed on the abdomen (see Fig. 2). The
Bloomlife wearable sensor was attached to the skin using a
medical grade adhesive patch. Secondly, we used the Porti7
device from Twente Medical Systems International (TMSi)
as a multi-accelerometer device for comparison. Accelerom-
eter data were bandpass filtered between 1and 20 Hz with
a second order butterworth IIR filter since fetal movement
is expected to be in this frequency band [12]. EMG data
were bandpassed filtered between 0.1 and 3 Hz to capture
low frequency maternal muscle activity on the abdomen. Five
accelerometer sensors of the TMSi device were positioned
on the abdomen with the navel serving as central marker. The
sixth sensor was placed on the back as reference for maternal
movement (see Fig. 2). All subjects were lying down in a
hospital bed and given a handheld toggle which they were
advised to press when feeling fetal movement. The output of
the button was always used as reference for fetal movements.
The experimenter manually annotated fetal movements as a
pre-processing step, by locating accelerometer movements
anticipating button triggers.
III. DATA ANALYS IS
A. Features
We computed the same feature sets for the TMSi (case
Multi) and Bloomlife (case Single) devices. To account for
different dynamics in maternal and fetal movement, we
computed features over two time windows of 0.5and 4
seconds. The rationale is that short fetal movements should
be averaged out over longer time windows but captured over
short ones, while maternal movements should appear over
windows of both durations. A time window of 0.5 seconds
was chosen as fetal kicks result in accelerations of short
duration, typically around 0.5 seconds, as shown in Fig.
1 [11]. On the other hand, the longer window was set to
4seconds as this duration is long enough to average out
accelerations due to fetal kicks (see Fig. 1) while being
short enough to limit processing delays and do not cause
maternal movements to impact algorithm output for longer
periods of time.Thus, variable-length features should reduce
false positives (see Fig. 1). We computed low-complexity
time domain features to possibly enable easy implementation
on an embedded device. Features were: mean, standard
deviation, interquartile range, correlation between axis, sum,
min, max and magnitude. Each feature was computed per
axis, per sensor and per window size. EMG features were
computed for Single and were the same time domain statistics
listed above for accelerometer data, computed over 4seconds
windows only. EMG is representative of muscular activity
on the abdomen and therefore, similarly to accelerometer
data, can capture maternal movement, albeit with slightly
different dynamics as the signal is typically slower than
almost instantaneous accelerometer spikes.
B. Features Selection, Class Imbalance and Classification
Classification was performed using random forests. Fea-
tures were not selected before classification, as random
forests can pick a subset of the available features at each
iteration. In particular, we set the number of features to
select at each iteration to the square root of the total number
of features, therefore potentially maintaining all information
at training phase, with respect to other features selection
methods. Given the small number of kicks with respect to the
total available data, class imbalance needs to be addressed
as well. Similarly to features selection, we let the random
forests classifier pick a subset of samples during training. The
optimal ratio between reference class (kicks) and majority
class (non-kicks) was determined by cross-validating and
optimizing for F-score, i.e. choosing the ratio that showed
optimal F-score. Our optimal balance included all data from
the minority class and one fifth of the majority class data.
C. Performance Metrics and Validation Method
We compared four feature sets associated to the two
systems used in this study in order to highlight the impact
of the novel methods proposed to improve accuracy of a
single wearable device in detecting fetal kicks by reducing
false positives. In particular, we compare; 1) TMSi (6 ac-
celerometer system) and variable-length features (Multi SL)
2) Bloomlife (single wearable sensor) and features computed
over a short (Single S) time window only 3) Bloomlife and
features computed over both short and long time windows
(Single SL) 4) Bloomlife and features computed over both
short and long time windows plus EMG features (Single
SLE). All models were derived and validated using leave one
participant out cross-validation and a binary classification
problem distinguishing fetal kicks and non-fetal kicks (e.g.
non-movement, noise, etc.). Given the binary classification
problem and data imbalance, we chose Sensitivity and PPV
as two metrics representative of the ability of the algorithms
to detect sporadic fetal kicks. Performance metrics were
determined according to the strategy depicted in Fig. 3 and
computed on the entire data stream for all participants during
cross-validation.
Fig. 3. Graphical example of our evaluation strategy. TP = true positive,
FN = false negative, FP = false positive.
Finally, we also provide plots on the relation between
detected and annotated fetal kicks over entire recordings for
each participant.
IV. RESULTS
Fig. 4 shows Sensitivity and PPV results for the four
models compared in this study; 1) TMSi (6 accelerometer
system) and variable-length features (Multi SL) 2) Bloomlife
(single wearable sensor) and features computed over short
(Single S) time window only 3) Bloomlife and features
computed over both short and long time windows (Single SL)
4) Bloomlife and features computed over both short and long
time windows plus EMG features (Single SLE). In particular,
Sensitivity was 0.74 for Multi SL,0.65 for Single S and
0.64 for Single SL and Single SLE. As expected Sensitivity
does not change much by introducing variable-length and
EMG features as the aim of these features is to reduce false
positives. On the other hand, PPV was 0.75 for Multi SL,0.65
for Single S,0.75 for Single SL and 0.76 for Single SLE,
highlighting how variable-length and EMG features could
consistently reduce false positives and increase PPV to the
same or higher values with respect to those obtained using
a6accelerometers system (Multi SL).
0
25
50
75
Multi SL Single S Single SLSingle SLE
Model
Percentage (%)
Sensitivity
0
25
50
75
Multi SL Single S Single SLSingle SLE
Model
Percentage (%)
PPV
Fig. 4. Sensitivity and PPV for the different models compared in this
study. Inclusion of features computed over longer time windows and EMG
data (case Single SLE) increases PPV (due to reduced false positives) to the
same levels shown for a multi sensor system consisting of 6accelerometers
(case Multi SL).
0
100
200
300
0 100 200 300
Detected kicks
Actual kicks
Actual vs Detected kicks, Multi SL
0
100
200
300
0 100 200 300
Detected kicks
Actual kicks
Actual vs Detected kicks, Single S
0
100
200
300
0 100 200 300
Detected kicks
Actual kicks
Actual vs Detected kicks, Single SL
0
100
200
300
0 100 200 300
Detected kicks
Actual kicks
Actual vs Detected kicks, Single SLE
Fig. 5. Relation between actual and detected kicks over each 20 minutes
recording for the different models compared in this study.
Finally, we report results on the relation between the
actual and detected total number of kicks over each entire
20-minute recording, Fig. 5, showing increased agreement
between the reference method (manual annotations) and the
models estimates as we add features, from Single S to Single
SLE, also for the number of kicks detected over entire
recordings.
V. DISCUSSION AND CONCLUSIONS
In this paper we proposed a method to improve the
accuracy of fetal kicks detection during pregnancy using
a single wearable device placed on the abdomen. The re-
sults obtained confirm our assumptions highlighted in Fig.
1. In particular, by including variable-length accelerometer
features, short fetal movement is averaged out over longer
time windows but captured over short ones, while maternal
movements of greater intensity appear over windows of both
durations. As a result, a single wearable device can be used
to better discriminate fetal and maternal movement without
the need for a reference accelerometer. Additionally, as EMG
is representative of muscular activity on the abdomen, it can
capture maternal movement, albeit with slightly different dy-
namics. We showed that the proposed method was effective
in reducing false positives by increasing PPV for a single
sensor device to the same levels obtained with a cumbersome
6sensors system (11% improvement).
One of the main limitations of this study is the use of
maternal annotations as reference as there is no trustworthy
reference for fetal movement. While ultrasound is the clinical
standard for fetal movement, limitations apply, even during
research studies. For example, with fetal growth it becomes
impossible to fully display the fetus given the limited field
of vision of the ultrasound probe, starting at approximately
week 20. While this is not a problem during hospital check-
ups, moving and re-positioning the probe while trying to
measure small accelerations as reflected on the pregnant
women abdomen is impractical and can easily introduce
noise. By analyzing the algorithms performance and trade
offs with respect to the same reference, we could get a
better understanding of the influence of different techniques
in effectively detecting fetal movement.
Finally, while false positive detection were reduced suc-
cessfully by the techniques employed, Sensitivity did not
improve using variable-length and EMG features. We spec-
ulate that reduced sensitivity for Single is due to lack of
spacial resolution as fetal kicks could be localized in specific
areas of abdomen. A practical solution to this issue could be
placement of the single wearable device where movement is
typically felt by the pregnant woman. Future work will focus
on some of these aspects as well as the possibility to include
different types of fetal movements.
REFERENCES
[1] E. Symonds, “On-line processing of the fetal electrocardiogram. a new
direction for fetal monitoring.” The Journal of reproductive medicine,
vol. 32, no. 7, pp. 509–512, 1987.
[2] H. P. van Geijn, “2 developments in ctg analysis,” Bailli`
ere’s clinical
obstetrics and gynaecology, vol. 10, no. 2, pp. 185–209, 1996.
[3] K. ˚
A. Salvesen, “Efsumb: safety tutorial: epidemiology of diagnos-
tic ultrasound exposure during pregnancy?european committee for
medical ultrasound safety (ecmus),” European journal of ultrasound,
vol. 15, no. 3, pp. 165–171, 2002.
[4] E. Sheiner, I. Shoham-Vardi, and J. S. Abramowicz, “What do clinical
users know regarding safety of ultrasound during pregnancy?” Journal
of ultrasound in medicine, vol. 26, no. 3, pp. 319–325, 2007.
[5] Z. Alfirevic, D. Devane, G. Gyte et al., “Continuous cardiotocography
(ctg) as a form of electronic fetal monitoring (efm) for fetal assessment
during labour,Cochrane Database Syst Rev, vol. 3, no. 3, 2006.
[6] R. M. Grivell, Z. Alfirevic, G. Gyte, and D. Devane, “Antenatal
cardiotocography for fetal assessment,” Cochrane Database Syst Rev,
vol. 1, 2010.
[7] B. Boashash, M. S. Khlif, T. Ben-Jabeur, C. E. East, and P. B.
Colditz, “Passive detection of accelerometer-recorded fetal movements
using a time–frequency signal processing approach,” Digital Signal
Processing, vol. 25, pp. 134–155, 2014.
[8] K. Nishihara, N. Ohki, H. Kamata, E. Ryo, and S. Horiuchi, “Auto-
mated software analysis of fetal movement recorded during a pregnant
woman?s sleep at home,” PloS one, vol. 10, no. 6, p. e0130503, 2015.
[9] G. Thomas, O. T. John, M. Mostefa, B. Boualem, C. Ian, W. Stephen,
F. Miguel, C. Susan, and C. Paul, “Detecting fetal movements using
non-invasive accelerometers: A preliminary analysis,” in Information
Sciences Signal Processing and their Applications (ISSPA), 2010 10th
International Conference on. IEEE, 2010, pp. 508–511.
[10] M. S. H. Khlif, B. Boashash, S. Layeghy, T. Ben-Jabeur, P. B. Colditz,
and C. East, “A passive dsp approach to fetal movement detection for
monitoring fetal health,” in Information Science, Signal Processing
and their Applications (ISSPA), 2012 11th International Conference
on. IEEE, 2012, pp. 71–76.
[11] M. Altini, P. Mullan, M. Rooijakkers, S. Gradl, J. Penders, N. Geusens,
L. Grieten, and B. Eskofier, “Detection of fetal kicks using body-worn
accelerometers during pregnancy: Trade-offs between sensors number
and positioning,” pp. 5319–5322, Aug 2016.
[12] S. Layeghy, G. Azemi, P. Colditz, and B. Boashash, “Non-
invasivemonitoring of fetal movements using time-frequency fea-
tures of accelerometry,” in Acoustics, Speech and Signal Processing
(ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp.
4379–4383.
[13] ——, “Classification of fetal movement accelerometry through time-
frequency features,” in Signal Processing and Communication Systems
(ICSPCS), 2014 8th International Conference on. IEEE, 2014, pp.
1–6.
[14] L. Minjie, T. Yongkang, L. Yunfeng, Y. Song, and D. Jingxin, “Fetal
movement detection based on mems accelerometer,une, vol. 13,
p. 15, 2016.
[15] “Bloomlife company website,” https://bloomlife.com/, accessed: 2016-
11-22.
... In the literature, there have been a number of research articles on automatic fetal movement recording by using different types of sensors placed on the maternal abdomen, including accelerometers [3] [4][5] [6], acoustic sensors [7], etc. This is based on the fact that fetal movement may produce sound waves or cause oscillations on the maternal abdominal wall [8]. ...
... A 4-second moving window with 75% overlapping is applied to perform signal segmentation. Another moving window of 10 seconds in length is also implemented simultaneously for maternal movement identification and elimination [6]. Threshold detection algorithm is implemented to exclude weak background noises as well as intensive impulses caused by maternal movements. ...
... In recent years, with the advancement of microelectronic technology as well as the development in signal processing, automatic detection of fetal movements by using accelerometers and advanced signal processing technologies has gained a lot of attention [9,10,11,12,13,14,15,16,17,18,19]. When contacted with the maternal abdominal wall, a movement of fetal body parts with sufficient force generates vibrations that could be detected by one or a set of accelerometers placed on the surface of the abdomen. ...
... Then, based on the extracted features, signal classification was performed to identify fetal movement signals from other signals. One point worth mentioning is that some previously published papers proposed an additional sensor placed on the mothers thigh or back to detect and eliminate maternal artifacts [11,14,15]. However, until now there is still no standard for the optimal placement of this reference sensor, and the integration of this additional sensor involves additional complexity to the monitoring system and thus brings limits to the use of this technique in real world applications. ...
Article
Full-text available
Objectives: This paper presents a novel wearable system for in-home and long-term fetal movement monitoring on a reliable and easily accessible basis. Material and methods: The system mainly consists of four accelerometers for fetal movement signal acquisition, a microcontroller for signal processing and an Android-based device interacting with the microcontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetal movement in an intelligible way. Results: The proposed system can deliver reliable results with a specificity of 0.99 and a sensitivity of 0.77 for fetal movement time series signal classification. Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medical professionals and hospital resources, and has potential applications in the field of e-Health home care. Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) help establish an initial database for future medical analysis of sensor-recorded fetal behaviors.
... Several fetal movement detection algorithms and devices have been proposed. Studies have been carried out to detect fetal movements utilizing modified beta distribution [5] and several methods have been proposed to detect fetal movements using devices [6] [7]. But the accuracy of fetal movement detection should be further improved. ...
Conference Paper
Fetal movement patterns are a measurement of fetal well-being. Therefore, it is important to ascertain fetal movements to avoid fetal morbidity and death. In this research, accelerometer data acquired from pregnant mothers were analyzed in order to recognize the fetal movement patterns. Identification of fetal movements from the accelerometer data is arduous due to the presence of mother’s respiratory movements and mother’s laugh signals in the data. Hence, time domain analysis was utilized to separate fetal movements from the data. The fetal movements were separated hierarchically by considering the Eigenvalues and Eigenvectors of the auto correlation matrix. The proposed method identified fetal movements with an accuracy of 95%. As the next scope of this work, it is expected to identify abnormalities in the fetal movements to predict the well-being of the fetus.
Article
Fetal movement (FM) is one of the important indexes of clinical observations on fetal activities (such as the position, duration and relative force of FM), and as demand for the home monitoring of pregnant women grows increasingly, a wearable device for the long-term monitoring of fetal movement has drawn more and more attention. This study integrated multi-point IMU sensing with an innovative real-time classification method to build a device for the long-term monitoring of fetal movement, including the evaluation of the relative position, force and duration. In order to validate the sensitivity and clinical feasibility of the device, this study performed phantom simulation tests and clinical tests on 13 pregnant women. The phantom test results showed that the device had high accuracy (>90.3%) in recognizing 12 FM positions, the relative force had high correlation ( ${R}^{2}>0.98$ ), and the duration of FM had a low error percentage (<10%). The clinical test results showed that the number of FMs detected by this method was coincident with the pregnant women’s self-perceptions, and the questionnaire result showed that the pregnant women highly accepted this device.
Chapter
With the availability of new and improved health care technologies suitable for at-home use, clinical diagnostics and care have, in recent years, started to make a slow transition from healthcare centers to the home. This transition promises to reduce the strain on the healthcare system, while at the same time offering patients the convenience of observation and possibly treatment from the comfort of their homes. New digital technologies for fetal health monitoring are also slowly finding their way into obstetric care, improving the quality of measured fetal health features and increasing patient comfort, hence enabling an improvement of the quality of care. Currently, a second wave of measurement technology improvements is underway, which promises to enable the move to unobtrusive continuous monitoring from the comfort of the home. In this chapter, a closer look is taken at the current state of fetal monitoring technologies, with all their pros and cons, followed by an overview of various fetal health monitoring devices which are currently becoming available for use in the clinic or at home. Finally, a detailed look is taken at the methods used and trade-offs made in the development of a device for at-home monitoring of fetal motion.
Article
Full-text available
Uterine contraction (UC) is an important clinical indictor for monitoring uterine activity. The purpose of this study is to develop a portable electrohysterogram (EHG) recording system (called PregCare) for monitoring UCs with EHG signals. The PregCare consisted of sensors, a signal acquisition device, and a computer with application software. Eight-channel EHG signals, the tocodynamometry (TOCO) signal, and maternal perception were recorded simultaneously by the signal acquisition device controlled by the computer via Bluetooth. PregCare was firstly evaluated by a signal simulator. Its relative error (RE) and coefficient of variation (CV) were calculated, and its agreement with the commercial instrument PowerLab was assessed by Bland–Altman plots. After that, PregCare was applied to 20 pregnant women in a hospital to record their EHG signals. These EHG signals were preprocessed and segmented into UCs and non-UCs. Then, the EHG features corresponding to UCs and non-UCs were extracted, respectively, including power spectral density (PSD), root mean square (RMS), peak frequency (PF), median frequency (MDF), and sample entropy (SamEn). One-way ANOVA was employed to assess the difference between UCs and non-UCs. The results show that RE and CV were less than 8% and 0.03%, respectively, which indicated the high accuracy and repeatability of PregCare. The small differences of mean and standard deviation indicated the high agreement between PregCare and PowerLab. Besides, the PSD of UCs was much larger than non-UCs between 0 and 0.7 Hz. RMS of UCs was significantly larger than non-UCs ( p<0.05 ). PF and SamEn of UCs were significantly smaller than non-UCs ( p<0.05 ). In conclusion, the developed EHG recording system was able to record EHG signals reliably. It has the advantages of portability, low power consumption, and wireless transmission, which can be used for long-term monitoring of UCs and prediction of the preterm delivery.
Article
Full-text available
Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote, self-administrated monitoring of fetal movement during pregnancy. However, many questions remain unanswered to date on the optimal setup in terms of body-worn accelerometers as well as signal processing and machine learning techniques used to detect fetal movement. In this paper, we systematically analyze the trade-offs between sensor number and positioning, the presence of reference accelerometers outside of the abdominal area and provide guidelines on dealing with class imbalance. Using a dataset of 15 measurements collected employing 6 three-axial accelerometers we show that including a reference ac-celerometer on the back of the participant consistently improves fetal movement detection performance regardless of the number of sensors utilized. We also show that two accelerometers plus a reference accelerometer are sufficient for optimal results.
Article
Full-text available
Fetal movement is an important biological index of fetal well-being. Since 2008, we have been developing an original capacitive acceleration sensor and device that a pregnant woman can easily use to record fetal movement by herself at home during sleep. In this study, we report a newly developed automated software system for analyzing recorded fetal movement. This study will introduce the system and compare its results to those of a manual analysis of the same fetal movement signals (Experiment I). We will also demonstrate an appropriate way to use the system (Experiment II). In Experiment I, fetal movement data reported previously for six pregnant women at 28-38 gestational weeks were used. We evaluated the agreement of the manual and automated analyses for the same 10-sec epochs using prevalence-adjusted bias-adjusted kappa (PABAK) including quantitative indicators for prevalence and bias. The mean PABAK value was 0.83, which can be considered almost perfect. In Experiment II, twelve pregnant women at 24-36 gestational weeks recorded fetal movement at night once every four weeks. Overall, mean fetal movement counts per hour during maternal sleep significantly decreased along with gestational weeks, though individual differences in fetal development were noted. This newly developed automated analysis system can provide important data throughout late pregnancy.
Conference Paper
Full-text available
This paper presents a time-frequency approach for fetal movement monitoring which is based on the instantaneous amplitude (IA) and instantaneous frequency (IF) of signals collected using 3axial accelerometers placed over the maternal abdomen. Results of a feature selection method based on receiver operating characteristic analysis shows that the mean of the IAs and deviation of the Ifs outperform other features. A support vector machine based classifier which uses these 2 features exhibits a total accuracy of 96.6% with reasonably high sensitivity and specificity.
Conference Paper
Full-text available
Monitoring fetal movement is important to assess fetal health. Standard clinical fetal monitoring technologies include ultrasound imaging and cardiotocography. Both have limited prognostic value and require significant health resources. We have recently developed a low-cost, passive, non-invasive system to monitor fetal activity, and therefore fetal health. This accelerometer-based system does not require trained operators and can be used outside a clinic. This work is a preliminary study to develop a method to automatically detect fetal movement using this new accelerometer system. We assess the efficacy of using a threshold method over a range of different frequency bands. We also examine using a set of statistical features for a detection method. Our results indicate that neither method performs sufficiently well to automatically detect fetal movement.
Article
Background Cardiotocography (CTG) is a continuous recording of the fetal heart rate obtained via an ultrasound transducer placed on the mother's abdomen. CTG is widely used in pregnancy as a method of assessing fetal well-being, predominantly in pregnancies with increased risk of complications. Objectives To assess the effectiveness of antenatal CTG (both traditional and computerised assessments) in improving outcomes for mothers and babies during and after pregnancy. Search methods We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (26 June 2015) and reference lists of retrieved studies. Selection criteria Randomised and quasi-randomised trials that compared traditional antenatal CTG with no CTG or CTG results concealed; computerised CTG with no CTG or CTG results concealed; and computerised CTG with traditional CTG. Data collection and analysis Two review authors independently assessed trials for inclusion and risk of bias, extracted data and checked them for accuracy. Main results Six studies (involving 2105 women) are included.Overall, the included studies were not of high quality, and only two had both adequate randomisation sequence generation and allocation concealment. All studies that were able to be included enrolled only women at increased risk of complications. Comparison of traditional CTG versus no CTG showed no significant difference identified in perinatal mortality (risk ratio (RR) 2.05, 95% confidence interval (CI) 0.95 to 4.42, 2.3% versus 1.1%, four studies, N = 1627, low quality evidence) or potentially preventable deaths (RR 2.46, 95% CI 0.96 to 6.30, four studies, N = 1627), though the meta-analysis was underpowered to assess this outcome. Similarly, there was no significant difference identified in caesarean sections (RR 1.06, 95% CI 0.88 to 1.28, 19.7% versus 18.5%, three trials, N = 1279, low quality evidence). There was also no significant difference identified for secondary outcomes related to Apgar scores less than seven at five minutes (RR 0.83, 95% CI 0.37 to 1.88, one trial, N = 396, very low quality evidence); or admission to neonatal special care units or neonatal intensive care units (RR 1.08, 95% CI 0.84 to 1.39, two trials, N = 883, low quality evidence), nor in the other secondary outcomes that were assessed. There were no eligible studies that compared computerised CTG with no CTG. Comparison of computerised CTG versus traditional CTG showed a significant reduction in perinatal mortality with computerised CTG (RR 0.20, 95% CI 0.04 to 0.88, two studies, 0.9% versus 4.2%, 469 women, moderate quality evidence). However, there was no significant difference identified in potentially preventable deaths (RR 0.23, 95% CI 0.04 to 1.29, two studies, N = 469), though the meta-analysis was underpowered to assess this outcome. There was no significant difference identified in caesarean sections (RR 0.87, 95% CI 0.61 to 1.24, 63% versus 72%, one study, N = 59, low quality evidence), Apgar scores less than seven at five minutes (RR 1.31, 95% CI 0.30 to 5.74, two studies, N = 469, very low quality evidence) or in secondary outcomes. Authors' conclusions There is no clear evidence that antenatal CTG improves perinatal outcome, but further studies focusing on the use of computerised CTG in specific populations of women with increased risk of complications are warranted.
Article
This paper describes a multi-sensor fetal movement (FetMov) detection system based on a time–frequency (TF) signal processing approach. Fetal motor activity is clinically useful as a core aspect of fetal screening for well-being to reduce the current high incidence of fetal deaths in the world. FetMov are present in early gestation but become more complex and sustained as the fetus progresses through gestation. A decrease in FetMov is an important element to consider for the detection of fetal compromise. Current methods of FetMov detection include maternal perception, which is known to be inaccurate, and ultrasound imaging which is intrusive and costly. An alternative passive method for the detection of FetMov uses solid-state accelerometers, which are safe and inexpensive. This paper describes a digital signal processing (DSP) based experimental approach to the detection of FetMov from recorded accelerometer signals. The paper provides an overview of the significant measurement and signal processing challenges, followed by an approach that uses quadratic time–frequency distributions (TFDs) to appropriately deal with the non-stationary nature of the signals. The paper then describes a proof-of-concept with a solution consisting of a detection method that includes (1) a new experimental set-up, (2) an improved data acquisition procedure, and (3) a TF approach for the detection of FetMov including TF matching pursuit (TFMP) decomposition and TF matched filter (TFMF) based on high-resolution quadratic TFDs. Detailed suggestions for further refinement are provided with preliminary results to establish feasibility, and considerations for application to clinical practice are reviewed.
Conference Paper
Fetal movement can help clinicians understand fetal functional development. Active methods for fetal monitoring such as ultrasound are expensive and there are objections to their long term usage. This paper presents a passive approach for fetal monitoring which uses solid state accelerometers placed on the mother's abdomen for the collection of fetal movements.
Article
Background: Cardiotocography (CTG) is a continuous recording of the fetal heart rate obtained via an ultrasound transducer placed on the mother's abdomen. CTG is widely used in pregnancy as a method of assessing fetal well-being, predominantly in pregnancies with increased risk of complications. Objectives: To assess the effectiveness of antenatal CTG (both traditional and computerised assessments) in improving outcomes for mothers and babies during and after pregnancy. Search methods: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (26 June 2015) and reference lists of retrieved studies. Selection criteria: Randomised and quasi-randomised trials that compared traditional antenatal CTG with no CTG or CTG results concealed; computerised CTG with no CTG or CTG results concealed; and computerised CTG with traditional CTG. Data collection and analysis: Two review authors independently assessed trials for inclusion and risk of bias, extracted data and checked them for accuracy. Main results: Six studies (involving 2105 women) are included. Overall, the included studies were not of high quality, and only two had both adequate randomisation sequence generation and allocation concealment. All studies that were able to be included enrolled only women at increased risk of complications.Comparison of traditional CTG versus no CTG showed no significant difference identified in perinatal mortality (risk ratio (RR) 2.05, 95% confidence interval (CI) 0.95 to 4.42, 2.3% versus 1.1%, four studies, N = 1627, low quality evidence) or potentially preventable deaths (RR 2.46, 95% CI 0.96 to 6.30, four studies, N = 1627), though the meta-analysis was underpowered to assess this outcome. Similarly, there was no significant difference identified in caesarean sections (RR 1.06, 95% CI 0.88 to 1.28, 19.7% versus 18.5%, three trials, N = 1279, low quality evidence). There was also no significant difference identified for secondary outcomes related to Apgar scores less than seven at five minutes (RR 0.83, 95% CI 0.37 to 1.88, one trial, N = 396, very low quality evidence); or admission to neonatal special care units or neonatal intensive care units (RR 1.08, 95% CI 0.84 to 1.39, two trials, N = 883, low quality evidence), nor in the other secondary outcomes that were assessed.There were no eligible studies that compared computerised CTG with no CTG.Comparison of computerised CTG versus traditional CTG showed a significant reduction in perinatal mortality with computerised CTG (RR 0.20, 95% CI 0.04 to 0.88, two studies, 0.9% versus 4.2%, 469 women, moderate quality evidence). However, there was no significant difference identified in potentially preventable deaths (RR 0.23, 95% CI 0.04 to 1.29, two studies, N = 469), though the meta-analysis was underpowered to assess this outcome. There was no significant difference identified in caesarean sections (RR 0.87, 95% CI 0.61 to 1.24, 63% versus 72%, one study, N = 59, low quality evidence), Apgar scores less than seven at five minutes (RR 1.31, 95% CI 0.30 to 5.74, two studies, N = 469, very low quality evidence) or in secondary outcomes. Authors' conclusions: There is no clear evidence that antenatal CTG improves perinatal outcome, but further studies focusing on the use of computerised CTG in specific populations of women with increased risk of complications are warranted.
Article
Digital filtering techniques can be applied to the recovery of the fetal electrocardiogram (ECG) from noise, using optimized digital filters matched to the frequency characteristics of the waveform. By forming a linear model of the fetal ECG, it is possible to measure time constants and characteristics on a real-time basis. The technique provides a powerful tool for the detection of subtle changes in the ECG, but the interpretation of these changes is critical to the application of the technique in fetal monitoring. Shifts in the ST segment and changes in T wave configuration are late indicators of fetal asphyxia, whereas the earliest indicators of stress are shortening of the PR interval and inversion of the normal positive relationship between the PR and RR intervals. The method appears to substantially enhance our ability to predict fetal stress at an early stage.
FHR monitoring has been the subject of many debates. The technique, in itself, can be considered to be accurate and reliable both in the antenatal period, when using the Doppler signal in combination with autocorrelation techniques, and during the intrapartum period, in particular when the FHR signal can be obtained from a fetal ECG electrode placed on the presenting part. The major problems with FHR monitoring relate to the reading and interpretation of the CTG tracings. Since the FHR pattern is primarily an expression of the activity of the control by the central and peripheral nervous system over cardiovascular haemodynamics, it is possibly too indirect a signal. In other specialities such as neonatology, anaesthesiology and cardiology, monitoring and graphic display of heart rate patterns have not gained wide acceptance among clinicians. Digitized archiving, numerical analysis and even more advanced techniques, as described in this chapter, have primarily found a place in obstetrics. This can be easily explained, since the obstetrician is fully dependent on indirectly collected information regarding the fetal condition, such as (a) movements experienced by the mother, observed with ultrasound or recorded with kinetocardiotocography (Schmidt, 1994), (b) perfusion of various vessels, as assessed by Doppler velocimetry, (c) the amount of amniotic fluid or (d) changes reflected in the condition of the mother, such as the development of gestation-induced hypertension and (e) the easily, continuously obtainable FHR signal. It is of particular comfort to the obstetrician that a normal FHR tracing reliably predicts the birth of the infant in a good condition, which makes cardiotocography so attractive for widespread application. However, in the intrapartum period, many traces cannot fulfil the criteria of normality, especially in the second stage. In this respect, cardiotocography remains primarily a screening and not so much a diagnostic method. As long as continuous monitoring of fetal acid-base balance has not been extensively tested in clinical practice, microblood sampling of the fetal presenting part (Saling, 1994) is a useful adjunct. The problem with non-normal tracings is that their significance is very often unclear. They may indicate serious fetal distress, finally resulting in preventable destruction of critical areas in the fetal brain and damage to various organs; or, on the contrary, they may indicate temporary changes in cardiovascular control as a reaction to the intermittent effects on fetal haemodynamics of, for example, uterine contractions, whether or not in combination with partial or complete compression of umbilical cord vessels or the vessels on the chorionic plate (van Geijn, 1994). Many factors influence the FHR and its variability, which further complicates the interpretation of FHR patterns; some have been discussed here in some detail. Undoubtedly, there is a need for quantitative and objective FHR analysis, as long as it does not lead to erroneous results. Close collaboration between engineers and clinicians is a prerequisite for further advances in this field. Decision support systems certainly have a future but only if they are able to take into account a large set of clinical data and can combine it with data obtained from FHR signals and other parameters referring to the fetal condition, such as fetal growth, Doppler velocimetry, amniotic fluid volume and biochemical and biophysical data obtained from the mother. Basic technical concepts inherent in computerized CTG analysis, such as sampling rate (Chang et al, 1995), signal loss, artefact detection (van Geijn et al, 1980), further processing of intervals, archiving in digitized format and monitor display, should receive considerable attention. There is still a long way to go until decision support systems find their way into obstetric practice. Further developments can only be achieved thanks to efforts of many basic and clinical researchers, wo