Key result
SVM classification of resting-state EEG achieves ~79% accuracy in distinguishing schizophrenia from healthy controls.
Why the study?
Interview-based schizophrenia diagnostic methods lack complete validity, schizophrenia is clinically heterogeneous, and supervised machine learning may help address these diagnostic challenges.
Does SVM-based classification of EEG data accurately discriminate schizophrenia from healthy controls and symptom subgroups?
Cross-Sectional
No
Does SVM-based classification of EEG data accurately discriminate schizophrenia from healthy controls and symptom subgroups?
SVM-based classification using EEG data can accurately discriminate schizophrenia from healthy controls and differentiate symptom subgroups, potentially improving diagnostic validity.
May support EEG biomarkers for schizophrenia diagnosis; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues. AIMS: To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. SETTINGS AND DESIGN: Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. MATERIALS AND METHODS: regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification. STATISTICAL ANALYSIS: Mann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications. RESULTS: Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances. CONCLUSIONS: SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.
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Tikka et al. (2020) conducted a cross-sectional in Schizophrenia. Support Vector Machine (SVM) classification using resting-state EEG features vs. Healthy controls and symptom subgroups was evaluated on Accuracy of classifying schizophrenia from healthy controls and positive from negative symptom subgroups. Support Vector Machine classification using resting-state EEG features achieved 78.95% accuracy in distinguishing schizophrenia from healthy controls and 89.29% for positive vs negative symptoms.
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