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Title: Fuzzy similarity index employing Lyapunov exponents for discrimination of EEG signals
Authors: Übeyli, Elif Derya
Keywords: fuzzy similarity index
chaotic signal
Lyapunov exponents
electroencephalogram (EEG) signals
Issue Date: 2006
Publisher: Acad Sciences Czech Republic, Inst Computer Science
Abstract: 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 the 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 the hippocampal formation of the opposite hemisphere; set D seizure-free intervals of five patients from the 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.
ISSN: 1210-0552
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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