Key result
EEG signal analysis using a Support Vector Machine (SVM) classifier detected schizophrenia with a classification rate of 90.14% and an overall accuracy of 89.58%.
Why the study?
Traditional diagnosis of schizophrenia through psychiatrist interviews is time consuming and prone to error, prompting the need for tools to help clinicians diagnose efficiently.
Can machine learning classifiers using EEG signal features accurately detect schizophrenia?
Can machine learning classifiers using EEG signal features accurately detect schizophrenia?
A machine learning approach using wavelet transforms and SVM on EEG signals achieved approximately 90% accuracy in detecting schizophrenia, suggesting its potential as a diagnostic biomarker.
Should not yet alter schizophrenia diagnostics; leaves open validation of EEG-SVM in larger cohorts.
Schizophrenia (SZ) is recognized by the United Nations as a serious mental disorder which is affected by 20 million people worldwide. The symptoms include combinations of hallucinations, delusions, and extremely disordered thinking and behaviour. SZ affects a person in every walks of his life and makes it difficult to move on. Traditionally the diagnosis of SZ is carried by a trained psychiatrist through careful and sharp patient interviews. This process is time consuming and may cause errors. Hence the aim of this work is help clinicians to carry out the diagnosis efficiently. The wavelet transforms which is a signal processing technique is used to analyze the nonlinear and non-stationary EEG signals. In wavelet analysis all the EEG signals has to be decomposed into approximation & detailed coefficients and using these wavelet coefficients four statistical features are calculated. T test class separability criteria is applied on these features and as a result IQR came out to be the best feature. This feature matrix is tested on various well-versed machine learning classifiers out of which SVM showed the best classification rate of 90.14% for SZ and an overall accuracy of 89.58%The results are a clear indication that EEG signals have proved to be a biomarker to discriminate SZ and will be a helpful tool for clinicians in future.
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Padayatty et al. (2022) studied Schizophrenia. EEG signal analysis using Support Vector Machine (SVM) classifier was evaluated on Classification rate for schizophrenia and overall accuracy. EEG signal analysis using a Support Vector Machine (SVM) classifier detected schizophrenia with a classification rate of 90.14% and an overall accuracy of 89.58%.
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