Automatic fetal movement recognition from multi-channel accelerometry data. (October 2021) - Mesbah, Mostefa; Khlif, Mohamed S.; Layeghy, Siamak; East, Christine E.; Dong, Shiying; Brodtmann, Amy; Colditz, Paul B.; Boashash, Boualem
Highlights: Significant health care resources are allocated to monitoring high risk pregnancies. Fetal movement is an important indicator of fetal health. Machine learning is used for detecting fetal movement from accelerometry data. Bagging using random forest as base classifier showed best fetal movement detection. Artefact removal and feature extraction were the key to the high detection performance. Abstract: Background and objective: Significant health care resources are allocated to monitoring high risk pregnancies to minimize growth compromise, reduce morbidity and prevent stillbirth. Fetal movement has been recognized as an important indicator of fetal health. Studies have shown that 25% of pregnancies with decreased fetal movement in the third trimester led to poor outcomes at birth. The studies have also shown that maternal perception of fetal movement is highly subjective and varies from person to person. A non-invasive system for fetal movement detection that can be used outside hospital would represent an advance in at-home monitoring of at-risk pregnancies. This is a challenging task that requires the use of advanced signal processing techniques to differentiate genuine fetal movements from contaminating artefacts. Methods: This manuscript proposes a novel algorithm for automatic fetal movement recognition using data collected from wearable tri-axial accelerometers strategically placed on the maternal abdomen. The novelty of the work resides in the efficient removal of artefacts and in distinctive feature extraction. The proposed algorithm used independent component analysis (ICA) for dimensionality reduction and artefact removal. A supplemental technique based on discrete wavelet transform (DWT) was also used to remove artefacts. Results: To identify fetal movements, 31 features were extracted from the acceleration data. Based on these features, several classifiers were used to distinguish fetal from non-fetal movements. Robustness of the classifiers was tested for various concentrations of artefacts in the classification data. The best performance was achieved by Bagging classifier algorithm, with random forest as its basis classifier, yielding an accuracy ranging from 87.6% to 95.8% depending on the artefact concentration level. Conclusions: A high performance detection of fetal movements can be achieved using accelerometery-based systems suitable for long-term monitoring.