Analysing complexity, variability and spectral measures of schizophrenic EEG signal
by Malihe Sabeti; Roya Behroozi; Ehsan Moradi
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 21, No. 2, 2016

Abstract: Symptoms, signs and disease progression are the mainstay of psychiatric disorders diagnosis but defining a biomarker would be a more accurate way for their diagnosis in future. Electroencephalogram (EEG) could be useful in identifying specific biomarkers for diagnosis severe psychiatric disorders. This study is aimed to compare three EEG analysis methods, complexity, variability and spectral measures for classification of schizophrenic and normal participants. Fifteen schizophrenic and 18 age-matched normal subjects participated in this study. For each case, 20 channels of EEG are recorded. The extracted features include two spectral measures such as spectral entropy (SpEn) and Reyni's entropy (ReEn), two complexity measures such as approximate entropy (ApEn) and Lempel-Ziv complexity (LZC) and a variability measure such as central tendency measure (CTM). Finally, k-nearest neighbour (k-NN) is used to classify two mentioned groups. Our results show the classification accuracy of 94% using leave-one (participant)-out cross validation, which improves the previous results in this manner and also simplifies the method. The result indicates the suggested features have a good sensitivity for detection of characteristic features of schizophrenia disorder.

Online publication date: Wed, 22-Jun-2016

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