Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns

Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetrically weighted local binary patterns (SLBP)-based automated approach for detection of schizophrenia in adolescents from electroencephalogram (EEG) signals. We extract SLBP-based histogram features fr...

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Bibliographic Details
Main Authors: Rajesh, Kandala. N V P S, Sunil Kumar, T.
Format: Conference Proceeding
Language:English
Subjects:
Adolescent
Algorithms
Biology
Classification algorithms
Databases, Factual
Diseases
Electroencephalography
Feature extraction
Histograms
Humans
Schizophrenia - diagnosis
Support Vector Machine
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Summary:Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetrically weighted local binary patterns (SLBP)-based automated approach for detection of schizophrenia in adolescents from electroencephalogram (EEG) signals. We extract SLBP-based histogram features from each of the EEG channels. These features are given to a correlation-based feature selection algorithm to get reduced feature vector length. Finally, the feature vector thus obtained is given to LogitBoost classifier to discriminate between schizophrenia and healthy EEG signals.The results validated on the publicly available database suggest that the SLBP effectively characterize the changes in EEG signals and are helpful for the classification of schizophrenia and healthy EEG signals with a classification accuracy of 91.66%. In addition, our approach has provided better results than the recently proposed approaches in schizophrenia detection.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9630232