Cited by
8 publications cite any version of this project or its primary paper, newest first.
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Wearable Electrohysterography Systems: A Systematic Review of Electrode Topology, Design Considerations, and Clinical Feasibility
Ressy Jaya Yanti, Eiji Kondoh, Tomohiko Igasaki. Sensors, 2026. Cites: paper -
Automated early preterm detection using hybrid multimodal attention network with gated fusion
Deepshikha Bhattacharya, Pratibha Singh, Anil Kumar Tiwari. Computers in Biology and Medicine, 2026. Cites: paper -
Analysis of Pregnancy Progression in Term Condition Using Propagation Features and Uterine EMG Measurements
S. Vinothini, N. Punitha, P. A. Karthick et al. IEEE Sensors Letters, 2026. Cites: paper -
A Physiologically Explainable Classifier for Labour Prediction Based on Electrohysterographical Signals
Dariusz S. Radomski, Zuzanna Oscik, Rafał Jóźwiak et al. Applied Sciences, 2025. Cites: paper -
Electrohysterography in modern obstetrics: Advances in signal processing, machine learning, and clinical applications
Katerina Barnova, Radek Martinek, Jitka Horakova et al. Artificial Intelligence in Medicine, 2025. Cites: paper -
A Comprehensive AI-Driven Approach Using EHG Signal Analysis for Delivery Type Prediction
Sahar Qaadan, Abdullah Ahmed, Reda AlSalman et al. IEEE International Forum on Research and Technology for Society and Industry (RTSI), 2025. Cites: paper -
Peak amplitude of the normalized power spectrum of the electromyogram of the uterus in the low frequency band is an effective predictor of premature birth
Žiga Pirnar, Franc Jager, Ksenija Geršak. PLoS ONE, 2024. Cites: paper -
Editorial: New technologies improve maternal and newborn safety
Jieyun Bai, Yaosheng Lu, Huishu Liu et al. Frontiers in Medical Technology, 2024. Cites: paper
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