一种用于在遗传性失神癫痫模型中检测癫痫发作的强大棘波和慢波算法。
A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model.
作者信息
Xanthopoulos Petros, Liu Chang-Chia, Zhang Jicong, Miller Eric R, Nair S P, Uthman Basim M, Kelly Kevin, Pardalos Panos M
机构信息
Industrial and Systems Engineering Department at University of Florida, Gainesville, FL 32611, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2184-7. doi: 10.1109/IEMBS.2009.5334941.
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.
动物模型在癫痫基础研究中被广泛应用。在许多研究中，需要对脑电图（EEG）记录所捕获的所有癫痫棘波和慢波放电（SWD）进行准确评分和量化。对长期EEG记录进行人工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专家花费大量时间。在本文中，我们采用了一种最初由作者提出用于检测人类失神（小发作）癫痫的SWD检测算法，来检测Fischer 334大鼠EEG记录中出现的SWD。该算法在阈值参数方面具有鲁棒性。将结果与人工评分进行比较，并讨论了不同阈值参数的影响。
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