Improved classification of term and preterm births from electrohysterogram signals using machine learning with model error features
Abstract Preterm birth (PTB) affects an estimated 15 million infants worldwide each year, raising an enormous challenge in the field of maternal-fetal medicine. This work explores machine learning approaches for distinguishing between term and preterm birth from three-channel electrohysterogram (EHG...
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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer
2026-03-01
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| Serie: | Discover Applied Sciences |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/s42452-026-08571-8 |
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