A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model.
A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model.
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一种强大的尖峰和波算法，用于检测遗传失神癫痫模型中的癫痫发作。
DOI：
10.1109/iembs.2009.5334941
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Pardalos,PanosM
中科院分区：
文献类型：
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作者：
Xanthopoulos,Petros;Liu,Chang-Chia;Zhang,Jicong;Miller,EricR;Nair,SP;Uthman,BasimM;Kelly,Kevin;Pardalos,PanosM
动物模型广泛用于基础癫痫研究。在许多研究中，需要准确地评分和量化脑电图（EEG）记录捕获的所有癫痫棘波和波放电（SWD）。长期EEG记录的手动评分是一项耗时且繁琐的任务，需要实验室人员和经验丰富的脑电图师花费大量时间。在本文中，我们适应的SWD检测算法，最初提出的作者缺席（小）癫痫发作检测在人类中，检测SWD出现在EEG记录Fischer 334大鼠。该算法是鲁棒的阈值参数。结果进行了比较，手动评分和不同的阈值参数的影响进行了讨论。
Animal models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.