Biomedical Engineering Department, nternational University of Vietnam National Universities, Ho Chi Minh City, Vietnam.
Comput Math Methods Med. 2012;2012:847686. doi: 10.1155/2012/847686. Epub 2012 Mar 5.
One of the inherent weaknesses of the EEG signal processing is noises and artifacts. To overcome it, some methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These methods reduced noises, but they were hazardous to patients. In this study, we propose using Lyapunov spectrum to filter noise and detect epilepsy on scalp EEG signals only. We determined that the Lyapunov spectrum can be considered as the most expected method to evaluate chaotic behavior of scalp EEG recordings and to be robust within noises. Obtained results are compared to the independent component analysis (ICA) and largest Lyapunov exponent. The results of detecting epilepsy are compared to diagnosis from medical doctors in case of typical general epilepsy.
脑电信号处理的固有弱点之一是噪声和伪迹。为了克服这一问题，最近文献中报道的一些癫痫预测方法基于对颅内脑电图（EEG）记录的混沌行为的评估。这些方法虽然减少了噪声，但对患者有一定风险。在这项研究中，我们提出仅使用李雅普诺夫谱来过滤噪声并检测头皮 EEG 信号中的癫痫。我们确定李雅普诺夫谱可以被认为是评估头皮 EEG 记录的混沌行为的最期望方法，并且在噪声中具有鲁棒性。获得的结果与独立成分分析（ICA）和最大李雅普诺夫指数进行了比较。检测癫痫的结果与典型全面性癫痫的医生诊断进行了比较。
