Automated Detection of Schizophrenia Using Optimal Wavelet-based Norm Features Extracted from Single-channel EEG

Overview

Journal Cogn Neurodyn

Publisher Springer

Specialty Neurology

Date 2021 Aug 9

PMID 34367367

Citations 19

Authors

Manish Sharma

U Rajendra Acharya

Affiliations

Soon will be listed here.

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 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.

Citing Articles

A comparative study of wavelet families for schizophrenia detection.

Sathiya E, Rao T, Kumar T Front Hum Neurosci. 2024; 18:1463819.

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Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.

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Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.

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Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

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