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Machine learning-based detection of in-utero fetal presentation from non-invasive fetal ECG

Title data

Jaeger, Katharina M. ; Nissen, Michael ; Richer, Robert ; Rahm, Simone ; Titzmann, Adriana ; Fasching, Peter A. ; Eskofier, Björn M. ; Leutheuser, Heike:
Machine learning-based detection of in-utero fetal presentation from non-invasive fetal ECG.
In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) : proceedings. - Piscataway, NJ : IEEE , 2022 . - 01-04
ISBN 978-1-6654-8791-7
DOI: https://doi.org/10.1109/BHI56158.2022.9926804

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Ambient Assisted Living and Medical Assistance Systems > Chair Ambient Assisted Living and Medical Assistance Systems - Univ.-Prof. Dr. Heike Leutheuser
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Ambient Assisted Living and Medical Assistance Systems
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works
600 Technology, medicine, applied sciences
Date Deposited: 02 Oct 2024 12:18
Last Modified: 11 Nov 2024 08:38
URI: https://eref.uni-bayreuth.de/id/eprint/90535