Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Abstract

Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.

How Wearable Sensors Can Support the Research on Foetal and Pregnancy Outcomes: A Scoping Review

“…Nine studies described the application of wearable sensors to collect information about foetal parameters [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. These studies are described in Table 1 , together with a summary of their design and population, wearable device used, and main findings.…”

“…However, recovering foetal movement signals from a continuous, low-amplitude, and heavily contaminated background is a challenging task, which requires advanced signal processing techniques to differentiate foetal movements from contaminating artefacts. To solve this issue, Liang and colleagues proposed a four-stage system that integrated foetal movement signal pre-processing, maternal artifact signal pre-identification, signal identification and classification [ 13 ]. The authors also compared the ability of two algorithms (i.e., orthogonal matching pursuit and adaptive filtering) to identify foetal movement signals, showing the best performance for the orthogonal matching pursuit algorithm [ 13 ].…”

“…To solve this issue, Liang and colleagues proposed a four-stage system that integrated foetal movement signal pre-processing, maternal artifact signal pre-identification, signal identification and classification [ 13 ]. The authors also compared the ability of two algorithms (i.e., orthogonal matching pursuit and adaptive filtering) to identify foetal movement signals, showing the best performance for the orthogonal matching pursuit algorithm [ 13 ]. In a second study, the authors refined their approach by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light Gradient Boosting Machine model [ 14 ].…”

“…In the first instance, included studies clearly demonstrated that non-invasive sensors, usually placed on the mother’s abdomen, are capable of detecting foetal heart rate and movements [ 12 , 13 , 14 , 15 , 16 , 17 , 19 , 20 ]. The task was not without objective difficulties, which arose when it became essential to differentiate between signals coming from the mother’s body and those coming from the foetus.…”

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording

“…Ultrasound technology can visually assess the health of a fetus and is the most widely clinical method of identifying FM [ 11 ]. However, this technique requires an experienced clinician to operate, and the prolonged use of ultrasound may cause potential harm to the fetus [ 12 , 13 , 14 ]. Advances in fetal magnetic resonance imaging (MRI) through cine-MRI scans allow for the direct monitoring of the movements of the entire fetus [ 15 ].…”