Article: Machine learning-based hybrid approach for schizophrenia diagnosis using non-stationary EEG signals Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2025 Vol.48 No.2 pp.155 - 171 Abstract: A serious mental illness, schizophrenia (SZ) affects 1% of people worldwide and is characterised by delusions, hallucinations and disorganised thought patterns. Diagnosis mostly is based on subjective interviews by a psychiatrist in which there is a high chance of human errors and bias. In this work, we have conducted a comprehensive analysis of electroencephalogram (EEG) data using empirical mode decomposition (EMD) algorithm which can analyse non-stationary and nonlinear signals and separates them into components at different resolutions called intrinsic mode functions (IMFs). In this work, our primary goal is to introduce a hybrid approach for IMF selection that combines four distinct parameters namely correlation, energy, statistical significance and power spectral density (PSD) distance. From the selected IMFs nine statistical features are computed and performance is evaluated using various classifiers. Among all the classifiers k-nearest neighbour (KNN) showed the best accuracy of 90.29% using the second IMF. These results suggest that EEG signals can effectively distinguish between healthy control and SZ patients and have a potential to help psychiatrists for diagnosis of SZ. Inderscience Publishers - linking academia, business and industry through research

Title: Machine learning-based hybrid approach for schizophrenia diagnosis using non-stationary EEG signals

Authors: Harasees Kaur; Padmavati Khandnor; Kanu Goel

Addresses: Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India ' Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India ' Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India

Abstract: A serious mental illness, schizophrenia (SZ) affects 1% of people worldwide and is characterised by delusions, hallucinations and disorganised thought patterns. Diagnosis mostly is based on subjective interviews by a psychiatrist in which there is a high chance of human errors and bias. In this work, we have conducted a comprehensive analysis of electroencephalogram (EEG) data using empirical mode decomposition (EMD) algorithm which can analyse non-stationary and nonlinear signals and separates them into components at different resolutions called intrinsic mode functions (IMFs). In this work, our primary goal is to introduce a hybrid approach for IMF selection that combines four distinct parameters namely correlation, energy, statistical significance and power spectral density (PSD) distance. From the selected IMFs nine statistical features are computed and performance is evaluated using various classifiers. Among all the classifiers k-nearest neighbour (KNN) showed the best accuracy of 90.29% using the second IMF. These results suggest that EEG signals can effectively distinguish between healthy control and SZ patients and have a potential to help psychiatrists for diagnosis of SZ.

Keywords: electroencephalogram; EEG; schizophrenia; empirical mode decomposition; EMD; k-nearest neighbour; KNN; machine learning.

DOI: 10.1504/IJBET.2025.147087

International Journal of Biomedical Engineering and Technology, 2025 Vol.48 No.2, pp.155 - 171

Received: 11 Jun 2024
Accepted: 03 Oct 2024
Published online: 10 Jul 2025 *

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Title: Machine learning-based hybrid approach for schizophrenia diagnosis using non-stationary EEG signals

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