基于 EEG 的新生儿癫痫分类的高斯混合模型。
Gaussian mixture models for classification of neonatal seizures using EEG.
机构信息
Department Electrical and Electronic Engineering, University College Cork, Ireland.
出版信息
Physiol Meas. 2010 Jul;31(7):1047-64. doi: 10.1088/0967-3334/31/7/013. Epub 2010 Jun 28.
Abstract
A real-time neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. The system includes feature transformation techniques and classifier output postprocessing. The detector was evaluated on a database of 20 patients with 330 h of recordings. A detailed analysis of the choice of parameters for the detector is provided. A mean good detection rate of 79% was obtained with only 0.5 false detections per hour. A thorough review of all misclassified events was performed, from which a number of patterns causing false detections were identified.
摘要
提出了一种基于高斯混合模型分类器的实时新生儿癫痫发作检测系统。该系统包括特征变换技术和分类器输出后处理。该检测器在一个包含 20 名患者 330 小时记录的数据库上进行了评估。对检测器参数的选择进行了详细的分析。在每小时仅检测到 0.5 个误报的情况下，平均良好检测率达到了 79%。对所有误分类事件进行了彻底的审查，从中确定了一些导致误报的模式。
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