Real time processing and analysis of fetal phonocardiographic signals

Acquisition Devices for Fetal Phonocardiography: A Scoping Review

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.

Time-frequency characteristics of the vibrations underlying the first fetal heart sound: a preliminary study

This work studied, for the first time, the time-frequency characteristics of the vibrations underlying the first fetal heart sound (S1). To this end, the continuous wavelet transform was used to produce time-energy and time-frequency representations of S1 from where five vibrations were studied by their timing, energy, and frequency characteristics in three gestational age groups (early, G1, preterm, G2, and term, G3). Results on a dataset of 1111 S1s (9 phonocardiograms between 33 and 40 weeks) indicate that such representations uncovered a set of five well-defined, non-overlapped, and large-energy vibrations whose features presented interesting behaviors. Thus, for each group, while the timing characteristics of the five vibrations were likely to be statically different, their frequencies were similar. Also, the energies of the vibrations were likely to be different only in G2 and G3. Alternatively, while the frequencies and energies of each vibration were likely to statistically change among groups (excluding the energy of the third vibration), the timings were more likely to change only from G1 to G2 and from G2 to G3. Therefore, this methodology seems suitable to detect and study the generating vibrations of S1. Future work will test the correlation between these vibrations and the valvular events.

Heart rate estimation and validation algorithm for fetal phonocardiography

Fetal heart rate (FHR) is an important parameter for assessing fetal well-being and is usually measured by doppler ultrasound. Fetal phonocardiography can provide non-invasive, easy-to-use and passive alternative for fetal monitoring method if reliable FHR measurements can be made even in noisy clinical environments. In this work we present an automatic algorithm to determine fetal heart rate from the fetal heart sound recordings in a noisy clinical environment. Using an electronic stethoscope fetal heart sounds were recorded from the expecting mother's abdomen, during weeks 30-40 of their pregnancy. Of these, 60 recordings were analyzed manually by two observers to obtain reference heart rate measurement. An algorithm was developed to determine FHR using envelope detection and autocorrelation of the signals. Algorithm performance was improved by implementing peak validation algorithm utilizing knowledge of valid FHR from prior windows and power spectral density function. The improvements in accuracy and reliability of algorithm were measured by mean absolute error and positive precent agreement. By including the validation step, the mean absolute error reduced from 11.50 to 7.54 beats per minute and positive percent agreement improved from 81% to 87%. The proposed algorithms provide good accuracy overall but are sensitive to the noises in recording environment that influence the quality of the signals.

Intelligent systems in obstetrics and midwifery: Applications of machine learning

INTRODUCTION

Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics.

METHODS

A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted.

RESULTS

Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.

CONCLUSIONS

This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

A study of a fetal heart rate calculation system based on R-R interval

BACKGROUND:

Fetal electrocardiogram (FECG) can be obtained in a non-invasive manner to monitor fetal growth status.

OBJECTIVE:

In this study, a fetal heart rate (FHR) calculation system was proposed, which consists of the FECG recorder (MF-HOLTER) and the FECG monitoring software (FECG-MS). The abdomen electrocardiogram (AECG) of pregnant woman is acquired through the MF-HOLTER. The FECG-MS packs the AECG data and calls the FECG separation algorithm to obtain the separated FECG and the fetal QRS (FQRS) position. The FHR is further obtained by calculating the R-R interval value. At the same time, this study proposed a FQRS position correction algorithm to calculate the correct FHR values.

METHOD:

In order to verify the accuracy of the FHR calculation, the ECG signal of FLUKE’s PS320 FETAL SIMULATOR and clinical data were simultaneously tested.

RESULTS:

The accuracy rate is over 98% in processing the simulator’s data. In processing clinical data, the FHR values obtained by both the system proposed in this study and Monica AN24 are very close, and the difference is less than 1 bpm.

CONCLUSION:

The results show that the FHR calculation system is accurate and stable, and has a positive application value and prospect.

Fetal heart rate estimation from a single phonocardiogram signal using non-negative matrix factorization

Fetal heart rate (FHR) is an important characteristic in fetal well-being follow-up. It is classically estimated using the cardiotocogram (CTG) non-invasive reference technique. However, this latter presents some significant drawbacks. An alternative non-invasive solution based on the fetal phonocardiogram (fetal PCG) can be used. But most of proposed methods based on the PCG signal need to detect and to label the fetal cardiac S₁ and S₂ sounds, which may be a difficult task in certain conditions. Therefore, in this paper, we propose a new methodology for FHR estimation from fetal PCG with one single cardio-microphone and without the distinction constraint of heart sounds. The method is based on the non-negative matrix factorization (NMF) applied on the spectrogram of fetal PCG considered as a source-filter model. The proposed method provides satisfactory results on a preliminary dataset of abdominal PCG signals. When compared to the reference CTG, correlation on FHR estimations between PCG and CTG is around 90%.

Machine Learning in Fetal Cardiology: What to Expect

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

FETAL HEART RATE VARIABILITY ANALYSIS FROM PHONOCARDIOGRAPHIC RECORDINGS

This paper presents an algorithm for classification of fetal health status using fetal heart rate variability (fHRV) analysis through phonocardiography. First, the fetal heart sound signals are acquired from the maternal abdominal surface using a specially developed Bluetooth-based wireless data recording system. Then, fetal heart rate (FHR) traces are derived from these signals. Ten numbers of linear and nonlinear features are extracted from each FHR trace. Finally, the multilayer perceptron (MLP) neural network is used to classify the health status of the fetus. Results show very promising performance toward the prediction of fetal wellbeing on the set of collected fetal heart sound signals. Finally, this work is likely to lead to an automatic screening device with additional potential of predicting fetal wellbeing.

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system.

Fetal breathing transmission in phonocardiographic monitoring telemedicine systems

The phonocardiographic monitoring of fetal heart activity due to its passive nature enables extremely long measuring times providing thus the possibility of fetal breathing recovery. The long monitoring time is required because of the temporary appearing of breathing movement. However, the long measurement time and consequently the large amount of data to be transmitted to the hospital's computer centre may be costly on mobile phone network. To keep monitoring costs low an appropriate data compression should be applied assuring the transmission of all important features of the detected acoustic signal. The present work summarizes the results of the extension of the novel telemedicine system with measurement of breathing periodicity and the achieved compression level of acquisited data. The Golomb-Rice compression is applied for lossless transmission of the segmented beat cycles considering the importance of the given segments in order to obtain the most accurate transfer of beat-to-beat time and all irregular heart sounds.

Long-term Phonocardiographic Fetal Home Monitoring for Telemedicine Systems

A novel compact method for fetal home monitoring optimized for long data acquisition time and low communication costs is presented. The method incorporates the preprocessing of disturbed acoustic signal received on the maternal abdomen. The basic idea of the preprocessing is that the detection of the systolic and diastolic sounds takes place on two separated frequency bands with autocorrelation on predicted time intervals. Measurements on 47 selected pregnant women have shown that the use of this method significantly reduces the amount of data to be transferred to the computer centre in the hospital, where only the very disturbed time periods have to be evaluated. Based on this method a new, phonocardiographic fetal telemedicine system can be built without time limitation of measurements.

Application of the matching pursuit method in a fetal phonocardiographic telemedicine system

A novel compact method for fetal home monitoring optimized for long data acquisition time has already been presented. The method involves preprocessing a disturbed acoustic signal received on the maternal abdomen. The present work summarizes phenomena encountered during analysis of the incoming PCG signal, and describes further improvements made to the analysis system already in use including the application of the MP method to determining heartbeat lengths and identifying murmurs between heart sounds.

Compact long-term recorder for the transabdominal foetal and maternal electrocardiogram

Foetal heart rate (FHR) monitoring is a proven means of assessing foetal health during the antenatal period. Currently, the only widely available instrumentation for producing these data is based on Doppler ultrasound, a technology that is unsuitable for long-term use. For nearly a century, it has been known that the foetal electrocardiogram (FECG) can be detected using electrodes placed on the maternal abdomen. Although these signals suggest an alternative means of FHR derivation, their use has been limited owing to problems of poor signal-to-noise ratio. However, the eminent suitability of the transabdominal FECG for long-term FHR monitoring has suggested that perseverance with the technique would be worthwhile. The paper describes the design, construction and use of a compact, long-term recorder of three channels of 24 h antenatal transabdominal data. Preliminary use of the recorder in around 400 short recording sessions demonstrates that FHR records of equivalent quality to those from Doppler ultrasound-based instruments can be extracted from such data. The success of FHR derivation is, on average, around 65% of the recording period from around 20 weeks gestation (although this figure is reduced from around 28-32 weeks, and the success rates exhibit a wide range when individual subjects are considered). These results demonstrate that the technique offers, not only a means of acquiring long-term FHR data that are problematic to obtain by other means, but also a more patient-friendly alternative to the Doppler ultrasound technique.

The feasibility of long-term fetal heart rate monitoring in the home environment using maternal abdominal electrodes

It is well established that fetal and maternal electrocardiograms (ECGs) can be obtained from the maternal abdomen using standard surface electrodes, although this cannot be guaranteed. The unobtrusive and non-invasive nature of such monitoring lends itself naturally to the long-term ambulatory collection of this data on cardiac activity. By employing suitable algorithms it would then be possible to extract records of both fetal and maternal heart rate. This article presents results of the collection of raw electrophysiological signals, containing both fetal and maternal ECGs from a single volunteer from the 20th week of gestation until term. The significance of the data is that they were recorded by the mother herself in her own environment. Previously written software was then used to extract fetal and maternal heart rate data. These results demonstrate the feasibility of using this method for the long-term recording of fetal and maternal heart rate in the mother's normal surroundings.

Neural network assisted cardiac auscultation

Traditional cardiac auscultation involves a great deal of interpretive skill. Neural networks were trained as phonocardiographic classifiers to determine their viability in this rôle. All networks had three layers and were trained by backpropagation using only the heart sound amplitude envelope as input. The main aspect of the study was to determine what topologies, gain and momentum factors lead to efficient training for this application. Neural networks which are trained with heart sound classes of greater similarity were found to be less likely to converge to a solution. A prototype normal/abnormal classifier was also developed which provided excellent classification accuracy despite the sparse nature of the training data. Future directions for the development of a full-scale computer-assisted phonocardiographic classifier are also considered.