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Automated detection of schizophrenia using optimal wavelet-based \(l_1\) norm features extracted from single-channel EEG

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Abstract

Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The \(l_1\) norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.

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Authors and Affiliations

  1. Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India

    Manish Sharma

  2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  4. Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan, ROC

    U. Rajendra Acharya

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  1. Manish Sharma
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  2. U. Rajendra Acharya
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Correspondence to Manish Sharma.

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Sharma, M., Acharya, U.R. Automated detection of schizophrenia using optimal wavelet-based \(l_1\) norm features extracted from single-channel EEG. Cogn Neurodyn 15, 661–674 (2021). https://doi.org/10.1007/s11571-020-09655-w

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