Publication Type : Conference Paper
Publisher : IEEE
Source : 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, DOI: 10.1109/EMBC44109.2020.9175892
Url : https://ieeexplore.ieee.org/document/9175892
Campus : Amritapuri
School : School for Sustainable Futures
Year : 2020
Abstract : The use of fetal heart rate (FHR) recordings for assessing fetal wellbeing is an integral component of obstetric care. Recently, non-invasive fetal electrocardiography (NI-FECG) has demonstrated utility for accurately diagnosing fetal arrhythmias via clinician interpretation. In this work, we introduce the use of data-driven entropy profiling to automatically detect fetal arrhythmias in short length FHR recordings obtained via NI-FECG. Using an open access dataset of 11 normal and 11 arrhythmic fetuses, our method (TotalSampEn) achieves excellent classification performance (AUC = 0.98) for detecting fetal arrhythmias in a short time window (i.e. under 10 minutes). We demonstrate that our method outperforms SampEn (AUC = 0.72) and FuzzyEn (AUC = 0.74) based estimates, proving its effectiveness for this task. The rapid detection provided by our approach may enable efficient triage of concerning FHR recordings for clinician review.
Cite this Research Publication : Keenan E; R. K. Udhayakumar, Karmakar CK, Brownfoot FC, Palaniswami M, Entropy profiling for detection of fetal arrhythmias in short length fetal heart rate recordings, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, DOI: 10.1109/EMBC44109.2020.9175892