Abstract
During pregnancy, fetal distress requiring clinical intervention can be difficult to accurately monitor and diagnose, necessitating technological improvements to bring clear information to patients and their care teams. Non-invasive fetal Electrocardiography (NI-fECG) monitoring may allow for earlier and more reliable detection of global cardiac ischemia due to hypoxia. However, the lowsignal-to-noise ratio of the fetal heartbeat relative to the maternal heartbeat remains a challenge. To enable reliable recognition of ischemia in NI-fECG, we propose an approach that combines simulating a pregnant torso and an unsupervised machine-learning method. Three stages of fetal cardiac ischemia: none (healthy), moderate, and severe, were introduced to the model. For each case, Electrocardiograms (ECGs) were simulated with the standard 12 leads, plus 3 additional abdominal leads. Unsupervised Multiple-Kernel Learning (MKL) with k-means clustering identified changes consistent with fetal cardiac ischemia despite noise from the parental heart. Thus, in this early proof-of-concept investigation, our results suggest that NI-fECG may offer a means for detecting global cardiac ischemia.
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Pérez, Á.J.B. et al. (2024). Non-Invasive Detection of Fetal Ischemia Through Electrocardiography. In: McCabe, K.J. (eds) Computational Physiology. Simula SpringerBriefs on Computing(), vol 17. Springer, Cham. https://doi.org/10.1007/978-3-031-53145-3_4
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DOI: https://doi.org/10.1007/978-3-031-53145-3_4
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