Key result
BiLSTM with attention achieves 68% accuracy for diagnosing schizophrenia using EEG data.
Why the study?
Conventional schizophrenia diagnosis techniques are time-consuming and prone to error, prompting the need for an automated EEG-based diagnostic technique with enhanced interpretability and prediction performance.
An interpretable BiLSTM-Attention deep learning model can classify schizophrenia using EEG data, though the reported accuracy in the text is 0.68%.
Hypothesis-generating for EEG ML in schizophrenia; leaves open clinical utility pending validation.
Introduction Schizophrenia is a severe psychological disorder that significantly impacts an individual’s life and is characterized by abnormalities in perception, behavior, and cognition. Conventional Schizophrenia diagnosis techniques are time- consuming and prone to error. The study proposes a novel automated technique for diagnosing Schizophrenia based on electroencephalogram (EEG) sensor data, aiming to enhance interpretability and prediction performance. Methods This research utilizes Deep Learning (DL) models, including the Deep Neural Network (DNN), Bi-Directional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM- GRU), and BiLSTM with Attention, for the detection of Schizophrenia based on EEG data. During preprocessing, SMOTE is applied to address the class imbalance. Important EEG characteristics that influence model decisions are highlighted by the interpretable BiLSTM-Attention model using attention weights in conjunction with SHAP and LIME explainability tools. In addition to fine-tuning input dimensionality, F-test feature selection increases learning efficiency. Results Through the integration of feature importance analysis and conventional performance measures, this study presents valuable insights into the discriminative neurophysiological patterns associated with Schizophrenia, advancing both diagnostic and neuroscientific expertise. The experiment’s findings show that the BiLSTM with attention mechanism model provides and accuracy of 0.68%. Discussion The results show that the recommended approach is useful for Schizophrenia diagnosis.
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Almadhor et al. (2025) studied Schizophrenia (n=81). BiLSTM with attention mechanism vs. None (direct evaluation of model performance) was evaluated on Accuracy of Schizophrenia diagnosis using EEG data. The BiLSTM with attention mechanism model achieved an accuracy of 68% for diagnosing Schizophrenia using EEG data.
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