Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies †

The effective prediction of preterm labor continues to be a topic of interest for research within pregnancy medicine, where uterine muscle contraction signals have shown to be insightful to predict a potential preterm birth. Magnetomyography (MMG) is a physiological-measurement-based tool which measures the orthogonal offset of bioelectrical manifestations from uterine contractions and may serve to predict potential premature deliveries with an enhanced accuracy. The decoding of the physiological signal is an area of substantial research where classical signal processing approaches and metaheuristics optimization routines have been utilized in the postprocessing and decomposition of MMG signals. This work requires a degree of expert knowledge and an understanding of tuning and parameter initialization. As a stride towards creating a more automated clinical decision support platform for predictions of preterm labor, we employ the use of a deep wavelet scattering (DWS) model. This methodology allows for a deep multiresolution analysis alongside unsupervised feature learning for the postprocessing of candidate MMG signals. DWS is combined with select pattern-recognition-based prediction machines in order to assemble a clinical decision pipeline for the prediction of the states of various pregnancies, with a greater degree of machine intelligence. The patient cohort consisted of a multi-ethnic demographic population composed of preterm and term pregnancies, where births occurred both under and over 48 h after labor commenced. Contrasting results were found between the various methods from the literature and DWS using the logistic regression algorithm. It was seen that DWS produced a slightly lower accuracy in comparison, as a trade-off for its streamlined unsupervised feature extraction process. Further work will now involve the application of various other machine learning methods in an attempt to assess and identify the most appropriate machine learning method with DWS that proves to be the most accurate.