Investigation of Methods to Extract Fetal Electrocardiogram from the Mother's Abdominal Signal in Practical Scenarios.

Tadesse Ghirmai,Michael P. H. Lau,Amir Mohammad Naderi,Brandon Tiang-Yu Kuo,Tai Le,Quoc-Dinh Nguyen,Huy-Dung Han,Hung Cao,Sadaf Sarafan

doi:10.3390/technologies8020033

Abstract

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA-TS-ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.

Highlights

Telemedicine and mobile health (m-Health) have been mentioned for more than a decade.only recently, have the wearable technology, internet of things (IoTs), computation powerTechnologies 2020, 8, 33; doi:10.3390/technologies8020033 www.mdpi.com/journal/technologiesTechnologies 2020, 8, 33 as well as telecommunication reached a point where these have become possible
The lowest F1 scores were found in extended Kalman filter (EKF), JADE, FastICA and RobustICA, with 54.34%, 61.27%, 60.08%, and 59.60%, respectively
It should be noted that using these algorithms alone was not as effective as when they were combined with other algorithms

Summary

Introduction

Telemedicine and mobile health (m-Health) have been mentioned for more than a decade.only recently, have the wearable technology, internet of things (IoTs), computation powerTechnologies 2020, 8, 33; doi:10.3390/technologies8020033 www.mdpi.com/journal/technologiesTechnologies 2020, 8, 33 as well as telecommunication (going to 5G and beyond) reached a point where these have become possible. A recent national study reported by the Centers for Disease Control (CDC) showed that the U.S. fetal mortality rate remained unchanged from 2006 through 2012 at 6.05 per 1000 births [1]. A key fetal monitoring parameter, which is fetal heart rate (fHR) via cardiotocography (CTG), despite being used in 85% of all labors in the U.S, and with comparable frequency during the antepartum period for monitoring, has not unequivocally shown that it can reduce perinatal mortality. The current CTG uses the Doppler ultrasound method to measure fHR. Such measurement could be challenging at times due to the need for precise alignment with the fetal heart to detect the fHR, which could be difficult when there are excessive maternal or fetal movements, or in the case of maternal obesity [2]

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Conclusion