Why the study?
Visual analysis of EEG carries interpretation issues, prompting the need for machine learning-based systems to detect schizophrenia at onset.
Does a machine learning based approach using EEG signals accurately distinguish between normal and schizophrenic subjects?
Does a machine learning based approach using EEG signals accurately distinguish between normal and schizophrenic subjects?
Machine learning algorithms, particularly Random Forest, show high accuracy in distinguishing schizophrenic subjects from normal subjects using resting-state EEG data.
EEG ML models may aid early schizophrenia detection; leaves open validation in prospective cohorts before clinical use.
The first signs of schizophrenia are thought to manifest during late adolescence. Hence, if the diagnosis can be made during the onset, then the patient can lead a comparatively functional life. The most cost-effective way to monitor the brain activity is using electroencephalography (EEG). Since the visual analysis of EEG comes with interpretation issues, researches are being carried out for machine learning based interpretation system. The authors proposed classification models using several machine learning algorithms to distinguish between normal and schizophrenic subjects from EEG data taken during the resting phase. The best result was by Random Forest (RF) with precision, sensitivity, and specificity of 0.965, 0.965, and 0.95 respectively.
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Sarkar et al. (2023) studied this question.
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