Automated Schizophrenia detection using local descriptors with EEG signals

Abstract

Schizophrenia (SZ) is a severe mental disorder characterized by behavioral imbalance and impaired cognitive ability. This paper proposes a local descriptors-based automated approach for SZ detection using electroencephalogram (EEG) signals. Specifically, we introduce a local descriptor, histogram of local variance (HLV), for feature representation of EEG signals. The HLV is generated by using locally computed variances. In addition to HLV, symmetrically weighted-local binary patterns (SLBP)-based histogram features are also computed from the multi-channel EEG signals. Thus, obtained HLV and SLBP-based features are given to a correlation-based feature selection algorithm to reduce the length of the feature vector. Finally, the reduced feature vector is fed to an AdaBoost classifier to classify SZ and healthy EEG signals. Besides, we have tested the influence of the different lobe regions in detecting SZ. For this, we combined the features extracted from channels belonging to the same group and performed the classification. Experimental results on two publicly available datasets suggest the local descriptors computed from temporal lobe channels are very effective in capturing regional variations of EEG signals. The proposed local-descriptors-based approach obtained an average classification accuracy of 92.85% and 99.36% on Dataset-1 and Dataset-2, respectively, with only a feature vector of length 13.