Early neonatal seizures detection is one of the most challenging issues in Neonatal Intensive Care Units. Several EEG-based Neonatal Seizure Detectors were proposed to support the clinical staff. However, less invasive and more easily interpretable methods than EEG are still missing. In this work, we investigated if Heart Rate Variability analysis and related measures as input features of supervised classifiers could be a valid support for discriminating between newborns with seizures and seizure-free ones. The proposed methods were validated on 52 subjects (33 with seizures and 19 seizure-free) of a public dataset collected at the Helsinki University Hospital. Encouraging results are achieved using a Linear Support Vector Machine, obtaining about 87% Area Under ROC Curve. This suggests that Heart Rate Variability analysis might be a non-invasive pre-screening tool to identify newborns with seizures.

HRV analysis: a non-invasive approach to discriminate between newborns with and without seizures / Frassineti, Lorenzo; Lanata, Antonio; Manfredi, Claudia. - ELETTRONICO. - 2021:(2021), pp. 52-55. ((Intervento presentato al convegno 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 [10.1109/EMBC46164.2021.9629741].

HRV analysis: a non-invasive approach to discriminate between newborns with and without seizures

Frassineti, Lorenzo;Lanata, Antonio;Manfredi, Claudia
2021

Abstract

Early neonatal seizures detection is one of the most challenging issues in Neonatal Intensive Care Units. Several EEG-based Neonatal Seizure Detectors were proposed to support the clinical staff. However, less invasive and more easily interpretable methods than EEG are still missing. In this work, we investigated if Heart Rate Variability analysis and related measures as input features of supervised classifiers could be a valid support for discriminating between newborns with seizures and seizure-free ones. The proposed methods were validated on 52 subjects (33 with seizures and 19 seizure-free) of a public dataset collected at the Helsinki University Hospital. Encouraging results are achieved using a Linear Support Vector Machine, obtaining about 87% Area Under ROC Curve. This suggests that Heart Rate Variability analysis might be a non-invasive pre-screening tool to identify newborns with seizures.
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Frassineti, Lorenzo; Lanata, Antonio; Manfredi, Claudia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1285857
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