ArticleBiomedical signal processing and control2026
A robust deep learning framework for automated fetal behavioral state classification: leveraging multi-center datasets to improve antepartum heart rate monitoring.
Article in Biomedical signal processing and control, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Fetal behavioral states reflect the developing brain's capacity to organize behavior into distinct sleep state patterns and are a window to evaluate the maturation of the nervous system throughout gestation. This study presents a deep learning-based methodology for the identification of behavioral states from fetal heart rate traces (FHR) recorded via cardiotocography (CTG). A Residual Attention U-Net was pre-trained on a large unlabeled dataset of over 7000 FHR recordings and fine-tuned on a multi-center dataset comprising 236 FHR signals with labeled states. These signals were collected across multiple countries using different cardiotocographs and annotated by clinicians with diverse backgrounds. The model showed high agreement with expert clinicians on a stratified hold-out test set, averaging a Macro F1-Score of 91% and Balanced Accuracy of 93% in distinguishing between
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