期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 210, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2021.106377
关键词
Fetal movement; Accelerometer; Artefact removal; Feature extraction; Classification
类别
资金
- Qatar National Research Fund [NPRP 096262243]
- Australian Research Council [DP1094498]
- National Health & Medical Research Council [NHMRC2006002416]
- Australian Research Council [DP1094498] Funding Source: Australian Research Council
This study proposed a novel automatic fetal movement recognition algorithm utilizing wearable tri-axial accelerometers placed on the maternal abdomen. By extracting multiple features and using various classifiers for identification and artefact removal, the Bagging classifier algorithm was found to perform the best in distinguishing fetal movements.
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 accelerometerybased systems suitable for long-term monitoring. (c) 2021 Elsevier B.V. All rights reserved.
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