Automated Schizophrenia Detection via Resnet50-based Ensemble Learning Using Multi-entropy EEG Feature Maps

Background/Aim: A complicated neuropsychiatric condition, schizophrenia is commonly investigated using one-dimensional EEG signals. In this work, a novel deep learning framework for automated schizophrenia detection is proposed, integrating an ensemble model based on ResNet50 with multi-entropy-based feature maps derived from 10-second EEG segments.Methods: Six entropy measures—approximate, bubble, fuzzy, permutation, sample, and spectral entropy—are computed to create two-dimensional EEG feature maps (EEG-FMs). A distinct ResNet50 model processes each EEG-FM, and soft voting is utilized for final predictions. The framework's effectiveness is evaluated using a leave-one-group-out cross-validation (LOGO-CV) method on two EEG datasets.Results: The ensemble framework exhibits high sensitivity and specificity, achieving average accuracies of 81.4% on Dataset 1 and 95.7% on Dataset 2. It evaluates the framework's performance across varied EEG distributions using a combined dataset and the Wilcoxon signed-rank test. The soft voting generally yields more balanced results than individual entropy-based models, although performance declines with increased dataset variability. An independent third EEG dataset (Dataset 3) is used to further assess the framework under differing conditions.Conclusion: The integration of entropy-based EEG features into the deep learning ensemble improves schizophrenia detection.