Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:52-55. doi: 10.1109/EMBC46164.2021.9629741.
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.Clinical Relevance- Heart Rate Variability analysis for detecting newborns with seizures in NICUs could speed up the diagnosis process and appropriate treatments for a better neurodevelopmental outcome of the infant.
早期新生儿癫痫发作的检测是新生儿重症监护病房中最具挑战性的问题之一。已经提出了几种基于脑电图的新生儿癫痫发作检测器来支持临床人员。然而，仍然缺乏比脑电图更具侵入性和更易于解释的方法。在这项工作中，我们研究了心率变异性分析和相关指标作为监督分类器的输入特征是否可以作为区分癫痫发作和无癫痫发作新生儿的有效支持。所提出的方法在赫尔辛基大学医院收集的一个公共数据集的 52 个对象（33 个有癫痫发作和 19 个无癫痫发作）上进行了验证。使用线性支持向量机获得了令人鼓舞的结果，获得了约 87%的ROC 曲线下面积。这表明心率变异性分析可能是一种非侵入性的预筛选工具，可以识别患有癫痫发作的新生儿。临床相关性-在新生儿重症监护病房中使用心率变异性分析来检测患有癫痫发作的新生儿，可以加快诊断过程，并为婴儿提供更好的神经发育结果的适当治疗。
