SIGNAL REGULARITY-BASED AUTOMATED SEIZURE DETECTION SYSTEM FOR SCALP EEG MONITORING.
SIGNAL REGULARITY-BASED AUTOMATED SEIZURE DETECTION SYSTEM FOR SCALP EEG MONITORING.
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DOI：
10.1007/s10559-010-9273-3
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发表时间：
2010-11-01
影响因子：
0.5
通讯作者：
Sackellares JC
中科院分区：
文献类型：
--
作者：
Shiau DS;Halford JJ;Kelly KM;Kern RT;Inman M;Chien JH;Pardalos PM;Yang MC;Sackellares JC
本研究的目的是建立一个临床上有用的自动癫痫发作检测系统的头皮脑电图记录。为了实现这一点，一个计算机算法的设计，复杂的多通道头皮EEG信号转化为几个动态描述符，其次是调查他们的时空属性，涉及到发作（癫痫发作）EEG模式，以及正常的生理和伪影信号。本文详细介绍了这种新的癫痫发作检测算法，并报告其在一个大型临床数据集的性能。
The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.