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Title:

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

Document type:
Article; Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Sarkar, Pritam; Lobmaier, Silvia; Fabre, Bibiana; González, Diego; Mueller, Alexander; Frasch, Martin G; Antonelli, Marta C; Etemad, Ali
Abstract:
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometric monitoring during pregnancy. A recently established self-supervised learning (SSL) approach to DL provides emotional recognition...     »
Journal title abbreviation:
Sci Rep
Year:
2021
Journal volume:
11
Journal issue:
1
Fulltext / DOI:
doi:10.1038/s41598-021-03376-8
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34921162
Print-ISSN:
2045-2322
TUM Institution:
Frauenklinik und Poliklinik
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