The University of Queensland, UQ Centre for Clinical Research, Herston QLD 4029, Australia.
Med Eng Phys. 2013 Dec;35(12):1762-9. doi: 10.1016/j.medengphy.2013.07.005. Epub 2013 Aug 21.
Neonatal EEG seizures often manifest as nonstationary and multicomponent signals, necessitating analysis in the time-frequency (TF) domain. This paper presents a novel neonatal seizure detector based on effective implementation of the TF matched filter. In the detection process, the TF signatures of EEG seizure are extracted to construct the TF templates used by the matched filter. Matching pursuit (MP) decomposition and narrowband filtering are proposed for the reduction of artifacts prior to seizure detection. Geometrical correlation is used to consolidate the multichannel detections and to reduce the number of false detections due to remnant artifacts. A data-dependent threshold is defined for the classification of EEG. Using 30 newborn EEG records with seizures, the classification process yielded an overall detection accuracy of 92.4% with good detection rate (GDR) of 84.8% and false detection rate of 0.36FD/h. Better detection performance (accuracy >95%) was recorded for relatively long EEG records with short seizure events.
新生儿脑电图发作通常表现为非平稳和多分量信号，因此需要在时频 (TF) 域中进行分析。本文提出了一种基于 TF 匹配滤波器有效实现的新型新生儿癫痫发作检测器。在检测过程中，提取 EEG 发作的 TF 特征，构建匹配滤波器使用的 TF 模板。在检测前，提出了匹配追踪 (MP) 分解和窄带滤波来减少伪迹。几何相关用于整合多通道检测，并减少由于残余伪迹引起的误报数量。定义了一个数据相关的阈值来对 EEG 进行分类。使用 30 份有癫痫发作的新生儿 EEG 记录，分类过程的总体检测准确率为 92.4%，检测率 (GDR) 为 84.8%，误报率为 0.36FD/h。对于具有短发作事件的相对较长 EEG 记录，检测性能更好（准确率>95%）。
