Fusing Tabular Features and Deep Learning for Fetal Heart Rate Analysis: A Clinically Interpretable Model for Fetal Compromise Detection.
- DOI
- 10.1109/tbme.2026.3652309
- Published
- 2026 Oct
- Container
- IEEE transactions on bio-medical engineering
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tbme.2026.3652309,
title = {Fusing Tabular Features and Deep Learning for Fetal Heart Rate Analysis: A Clinically Interpretable Model for Fetal Compromise Detection.},
author = {Mendis L and Karmakar D and Palaniswami M and Brownfoot F and Keenan E},
year = {2026},
journal = {IEEE transactions on bio-medical engineering},
doi = {10.1109/tbme.2026.3652309},
url = {https://doi.org/10.1109/tbme.2026.3652309}
}RIS
TY - JOUR TI - Fusing Tabular Features and Deep Learning for Fetal Heart Rate Analysis: A Clinically Interpretable Model for Fetal Compromise Detection. AU - Mendis L AU - Karmakar D AU - Palaniswami M AU - Brownfoot F AU - Keenan E PY - 2026 JO - IEEE transactions on bio-medical engineering DO - 10.1109/tbme.2026.3652309 UR - https://doi.org/10.1109/tbme.2026.3652309 ER -
APA
L, M., D, K., M, P., F, B., & E, K. (2026). Fusing Tabular Features and Deep Learning for Fetal Heart Rate Analysis: A Clinically Interpretable Model for Fetal Compromise Detection.. IEEE transactions on bio-medical engineering. https://doi.org/10.1109/tbme.2026.3652309
Source records
- pubmed · retrieved 2026-11-10T15:48:49.851Z