Key result
EEG-based machine learning achieves ~99% accuracy in identifying schizophrenia.
Why the study?
A low-cost, accurate, and easy-to-implement diagnostic system for schizophrenia using EEG signals is needed for timely treatment.
Does a fusion of pattern-based and statistical features from EEG signals accurately detect Schizophrenia?
Does a fusion of pattern-based and statistical features from EEG signals accurately detect Schizophrenia?
A novel machine learning framework using EEG signals demonstrates high accuracy (>98%) for the detection of Schizophrenia.
May support EEG-based schizophrenia screening research; leaves open prospective clinical validation before diagnostic adoption.
Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.
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Agarwal et al. (2023) studied Schizophrenia. Fusion of pattern-based and statistical features from EEG signals using boosted trees classifier was evaluated on Accuracy in identifying Schizophrenia cases. A proposed machine learning framework using a fusion of pattern-based and statistical features from EEG signals achieved 98.62% and 99.24% accuracy in identifying schizophrenia across two datasets.
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