20.500.12556/RUL-162193 Peak amplitude of the normalized powerspectrum of the electromyogram of theuterus in the low frequency band is aneffective predictor of premature birth The current trends in the development of methods for non-invasive prediction of premature birth based on the electromyogram of the uterus, i.e., electrohysterogram (EHG), suggest an ever-increasing use of large number of features, complex models, and deep learning approaches. These “black-box” approaches rarely provide insights into the underlying physiological mechanisms and are not easily explainable, which may prevent their use in clinical practice. Alternatively, simple methods using meaningful features, preferably using a single feature (biomarker), are highly desirable for assessing the danger of premature birth. To identify suitable biomarker candidates, we performed feature selection using the stabilized sequential-forward feature-selection method employing learning and validation sets, and using multiple standard classifiers and multiple sets of the most widely used features derived from EHG signals. The most promising single feature to classify between premature EHG records and EHG records of all other term delivery modes evaluated on the test sets appears to be Peak Amplitude of the normalized power spectrum (PA) of the EHG signal in the low frequency band (0.125-0.575 Hz) which closely matches the known Fast Wave Low (FWL) frequency band. For classification of EHG records of the publicly available TPEHG DB, TPEHGT DS, and ICEHG DS databases, using the Partition-Synthesis evaluation technique, the proposed single feature, PA, achieved Classification Accuracy (CA) of 76.5% (AUC of 0.81). In combination with the second most promising feature, Median Frequency (MF) of the power spectrum in the frequency band above 1.0 Hz, which relates to the maternal resting heart rate, CA increased to 78.0% (AUC of 0.86). The developed method in this study for the prediction of premature birth outperforms single-feature and many multi-feature methods based on the EHG, and existing non-invasive chemical and molecular biomarkers. The developed method is fully automatic, simple, and the two proposed features are explainable. preterm birth pregnancy electromyogram of the uterus biomarkers signal processing power spectrum deep learning prezgodnji porod nosečnost elektromiogram maternice biomarkerji procesiranje signalov močnostni spekter globoko učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-09-19 14:02:43 2024-09-19 14:02:44 2024-09-23 03:36:38 0000-00-00 00:00:00 2024 0 0 38 str. iss. 9, art. e0308797 Vol. 19 Sep. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:618.39 1932-6203 10.1371/journal.pone.0308797 208113411 RAZ_Pirnar_Ziga_2024.pdf RAZ_Pirnar_Ziga_2024.pdf 1 D1105C3E484426D0923E4BE5CE01A241 56b34271fa569d32e854a4c7d24ef4d9da55f40b09e6697f76669b4815803cdc ca290b92-767f-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=191372 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308797 1 f91496b4-767e-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=191371 Fakulteta za računalništvo in informatiko Medicinska fakulteta 0 0 0