Ubeyli Elif Derya
Department of Electrical & Electronics Engineering, TOBB Ekonomi ve Teknoloji Universitesi, Ankara, Turkey.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:5346-9. doi: 10.1109/IEMBS.2006.259316.
In this study, a new approach based on the computation of fuzzy similarity index was presented for discrimination of electroencephalogram (EEG) signals. The EEG, a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. The analyzed EEG signals were consisted of five sets (set A-healthy volunteer, eyes open; set B-healthy volunteer, eyes closed; set C-seizure-free intervals of five patients from hippocampal formation of opposite hemisphere; set D-seizure-free intervals of five patients from epileptogenic zone; set E-epileptic seizure segments). The EEG signals were considered as chaotic signals and this consideration was tested successfully by the computation of Lyapunov exponents. The computed Lyapunov exponents were used to represent the EEG signals. The aim of the study is discriminating the EEG signals by the combination of Lyapunov exponents and fuzzy similarity index. Toward achieving this aim, fuzzy sets were obtained from the feature sets (Lyapunov exponents) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the EEG signals. Thus, the fuzzy similarity index could discriminate the healthy EEG segments (sets A and B) and the other three types of segments (sets C, D, and E) recorded from epileptic patients.
在本研究中，提出了一种基于模糊相似性指数计算的新方法，用于区分脑电图（EEG）信号。EEG是一种高度复杂的信号，是用于研究脑功能和神经疾病的最常见信息源之一。所分析的EEG信号由五组组成（A组——健康志愿者，睁眼；B组——健康志愿者，闭眼；C组——来自对侧海马结构的五名患者的无癫痫发作间期；D组——来自致痫区的五名患者的无癫痫发作间期；E组——癫痫发作片段）。EEG信号被视为混沌信号，通过计算李雅普诺夫指数成功验证了这一观点。计算得到的李雅普诺夫指数用于表示EEG信号。本研究的目的是通过结合李雅普诺夫指数和模糊相似性指数来区分EEG信号。为实现这一目标，从所研究信号的特征集（李雅普诺夫指数）中获得模糊集。结果表明，所研究信号的模糊集之间的相似性表明了EEG信号的变异性。因此，模糊相似性指数可以区分健康EEG片段（A组和B组）以及癫痫患者记录的其他三种类型的片段（C组、D组和E组）。
