HRV-based Monitoring of Neonatal Seizures with Machine Learning

Lu H, Kusnik S, Mammadova D, Trollmann R, Koelpin A (2024)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2024

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Series: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Conference Proceedings Title: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Event location: Orlando, FL US

ISBN: 979-8-3503-7150-5

DOI: 10.1109/EMBC53108.2024.10782590

Abstract

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.

Authors with CRIS profile

Hui Lu Sonderforschungsbereich 1483/1 - Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (SFB EmpkinS) Stefan Kusnik Department of Paediatrics and Adolescent Medicine Dilbar Strack Department of Paediatrics and Adolescent Medicine Regina Trollmann Professur für Kinder- u. Jugendmedizin Schwerpunkt Neuropädiatrie

Involved external institutions

Technische Universität Hamburg-Harburg (TUHH) DE Germany (DE)

How to cite

APA:

Lu, H., Kusnik, S., Mammadova, D., Trollmann, R., & Koelpin, A. (2024). HRV-based Monitoring of Neonatal Seizures with Machine Learning. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Lu, Hui, et al. "HRV-based Monitoring of Neonatal Seizures with Machine Learning." Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024, Orlando, FL Institute of Electrical and Electronics Engineers Inc., 2024.

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