Abstract
Until recently, many segmentation research trials on uterine EMG have been recorded for the sake of not only automatic detection of contractions but also curtailment of other events presents in the electrohysterogram (EHG). In this study, we use an online segmentation method, which has proven its efficiency and known by Dynamic Cumulative Sum (DCS). The method is first applied on real bipolar EHGs signals then on their details obtained by wavelet transform. The detected rupture instants are driven through an enhanced technique of faulty rupture instants elimination, dynamic selection of multichannel bipolar EHG signals and its details after wavelet transform and event tracking algorithm. Therefore, enhanced method sensitivity and “other events” rate of bipolar EHGs with and without wavelet are computed using Margin validation test in order to classify among events as contractions or not referring to contractions identified by expert. Indeed, enhanced technique of event tracking, proposed in this study, seems to be more efficient comparing to previous techniques. Further studies should be achieved for the sake of fully identifying the uterine contractions from other events and then decreasing the “other events” rate.
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This research is supported by Lebanese University and Al Koura Hospital.
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Zaylaa, A., Diab, A., Fawal, Z., Khalil, M., Marque, C. (2020). Automatic Segmentation of Bipolar EHGs’ Contractions Using Wavelet Transform. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_21
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