Chua C K, Chandran V, Acharya Rajendra, Lim C M
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:6496-9. doi: 10.1109/IEMBS.2007.4353847.
Epilepsy is a neurological condition, which affects the nervous system. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the Electroencephalogram (EEG). Seizures are the clinical manifestations of excessive and hypersynchronous activity of the neurons in the cerebral cortex and represent one of the most frequent malfunctions of the human central nervous system. Therefore, the search for precursors and predictors of a seizure in the human EEG is of utmost clinical relevance and may even lead to a deeper understanding of the seizure generating mechanisms. In this paper, the normal, pre-ictal (background) and ictal (epileptic) EEG signals are studied using higher order spectra. HOS based measures are shown to be able to distinguish epileptic EEG from normal and background EEG with high confident level (p-value of less than 0.05).
癫痫是一种影响神经系统的神经疾病。自动癫痫发作检测在临床实践中非常重要,必须通过分析脑电图(EEG)来实现。癫痫发作是大脑皮层神经元过度和超同步活动的临床表现,是人类中枢神经系统最常见的故障之一。因此,在人类脑电图中寻找癫痫发作的先兆和预测因素具有至关重要的临床意义,甚至可能有助于更深入地理解癫痫发作的产生机制。在本文中,使用高阶谱对正常、发作前(背景)和发作期(癫痫)脑电图信号进行了研究。基于高阶谱的测量方法能够以高置信水平(p值小于0.05)将癫痫脑电图与正常和背景脑电图区分开来。