Options
HRV-based Monitoring of Neonatal Seizures with Machine Learning
Publikationstyp
Conference Paper
Date Issued
2024-07
Sprache
English
Author(s)
Kusnik, Stefan
Trollmann, Regina
Citation
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2024)
Contribution to Conference
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Publisher DOI
Publisher
IEEE
With the rapid development of machine learning (ML) in biomedical signal processing, ML-based neonatal seizure detection using heart rate variability (HRV) parameters extracted from the electrocardiogram (ECG) has gained increasing interest. In this paper, we present a benchmarking of various ML classifiers for HRV-based neonatal seizure monitoring. We extract the HRV parameter in time-domain, frequency-domain, and nonlinear-domain from segments with duration ranging from 30 to 180 s and perform the feature selection with minimum redundancy and maximum relevance (mRmR). In the next step, we evaluate the performance using nested cross-validation on a dataset collected from 16 preterm and term newborns with neonatal seizures with a total duration of over 35 hours. The best-performing classifier was the support vector machine (SVM) with a linear kernel using HRV parameters from the 180 s segment, achieving an area under the operator characteristic operating curve (AUC) score of 0.627, 89.7% sensitivity, 34.6% specificity, and 92.3% good detection rate.