Complexity of EEG Signals in Schizophrenia Syndromes

Abstract

In the present study, 45 patients with schizophrenia syndromes and 39 healthy subjects are studied with electroencephalogram (EEG) signals. The study groups were of different genders. For each of the two groups, the signals were analyzed using 16 EEG channels. Multiscale entropy, Lempel-Ziv complexity and Lyapunov exponent were used to study the chaotic signals. The data were compared for two groups of subjects. Entropy was compared for each of the 16 channels for all subjects. As a result, topographic images of brain areas were obtained, illustrating the entropy and complexity of Lempel-Ziv. Lempel-Ziv complexity was found to be more representative of the classification problem. The results will be useful for further development of EEG signal classification algorithms for machine learning. This study shows that EEG signals can be an effective tool for classifying participants with symptoms of schizophrenia and control group. It is suggested that this analysis may be an additional tool to help psychiatrists diagnose patients with schizophrenia.