Abstract
Intelligent systems are now part of human daily life. This justifies its application in various fields. However, the field of psychiatry still seems to be at a disadvantage. In this paper, we are interested in measures that can be used as input to an intelligent system for detecting brain diseases using an electroencephalogram (EEG). Indeed, spectral analysis and the study of brain connectivity are two methods of EEG analysis that can be used to characterize schizophrenia. The spectral analysis allows to calculate the power (absolute and relative), the frequency peak among others. Concerning the study of cerebral connectivity, the Phase Lag Index (PLI), which is an adjacency matrix ; which is an adjacency matrix used to assess brain connectivity. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. On the other hand, the value of strength is always lower in sick people than in healthy people. This is true regardless of the frequency band or channel used. This study shows that values such as degree, density, strength of a predefined adjacency matrix, then power, peak frequency band can be used as input values of an intelligent system for diagnosing psychiatric or brain diseases such as schizophrenia.
Highlights
Smoking, alcoholism, sedatives and drugs can be some of the causes of some mental or psychiatric illnesses such as depression, dementia and schizophrenia [1]
The challenge in this work is to evaluate, analyze the density, degree and strength on each channel, of each Phase Lag Index (PLI), calculated on different frequency bands, in order to compare people suffering from schizophrenia compared to healthy
An analysis using a 3-way ANOVA revealed, among other things, that there is a significant difference between the degree of patients and that of healthy control
Summary
Alcoholism, sedatives and drugs can be some of the causes of some mental or psychiatric illnesses such as depression, dementia and schizophrenia [1]. Tianyi Yan and Wenhui Wang [16] study the event phase synchronization, represented by the phase Lag Index (PLI), and suggest that patients with schizophrenia present specific alterations, indicated by an increased local connectivity in the gamma oscillations during facial treatment This same paper shows that the sick people had a reduced functional connectivity of the beta band through the frontal regions and of the gamma band through the scalp compared to the control subjects. Elzbieta Olejarczyk and Jernajczyk [5]suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, could be a useful tool in the study of connectivity disorders such as schizophrenia It shows that the analysis of the resting EEG showed an increase in delta, theta and beta activity, as well as a decrease in alpha frequency activity in the frontal cortex ("hypofrontality") in patients versus controls. Because the final goal is to set up an Artificial Intelligence allowing the diagnosis of schizophrenia
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