Deep residual network-based feature extraction combined with a support vector machine classifier achieved 99.23% accuracy in automatically detecting schizophrenia from EEG signals.
Does a deep residual network (deep ResNet) based feature extraction improve the accuracy of automatic schizophrenia detection from EEG signals compared to traditional methods?
A deep residual network-based feature extraction method combined with an SVM classifier achieved 99.23% accuracy in automatically detecting schizophrenia from EEG signals.
Absolute Event Rate: 99.23% vs 97.48%
Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.
Siuly et al. (2023) studied Schizophrenia (n=81). Deep Residual Network (deep ResNet) feature extraction with Support Vector Machine (SVM) classifier vs. Deep ResNet with softmax classifier was evaluated on Classification accuracy for schizophrenia detection. Deep residual network-based feature extraction combined with a support vector machine classifier achieved 99.23% accuracy in automatically detecting schizophrenia from EEG signals.
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