A CNN-Transformer Fusion Model for Proactive Detection of Schizophrenia Relapse from EEG Signals

Abstract

Proactively detecting schizophrenia relapse remains a critical challenge in psychiatric care, where traditional predictive models often fail to capture the complex neurophysiological and behavioral dynamics preceding recurrence. Existing methods typically rely on shallow architectures or unimodal data sources, resulting in limited sensitivity-particularly in the early stages of relapse. In this study, we propose a CNN-Transformer fusion model that leverages the complementary strengths of Convolutional Neural Networks (CNNs) and Transformer-based architectures to process electroencephalogram (EEG) signals enriched with clinical and sentiment-derived features. This hybrid framework enables joint spatial-temporal modeling of relapse indicators, allowing for a more nuanced and patient-specific analysis. Unlike previous approaches, our model incorporates a multi-resource data fusion pipeline, improving robustness, interpretability, and clinical relevance. Experimental evaluations demonstrate a superior prediction accuracy of 97%, with notable improvements in recall and F1-score compared to leading baselines. Moreover, the model significantly reduces false negatives, a crucial factor for timely therapeutic intervention. By addressing the limitations of unimodal and superficial prediction strategies, this framework lays the groundwork for scalable, real-world applications in continuous mental health monitoring and personalized relapse prevention.

Keywords: personalized relapse prevention; robustness; schizophrenia disorder; transformer model; unimodal data.

Figure 2

Conceptual mind map outlining the enabling technologies, emerging trends, challenges, and opportunities in optimizing schizophrenia relapse prediction through deep-learning (DL) models. The diagram organizes key factors into five distinct categories: (I) Key Enabling Technologies, (II) Trends, (III) Challenges, (IV) Opportunities, and (V) Ethical and Infrastructure Considerations. Each category further highlights specific subdomains relevant to clinical integration, data management, and intelligent health care delivery.

Figure 3

EEG signal processing pipeline in relation to mental health prediction. It shows the workflow from EEG raw acquisition and pre-processing to time-frequency and feature extraction. The listed features are then used by discriminative models (SVM, LDA, RF, GRU) and deep learning models (GAN, DBN, CNN, LSTM, GRU) for classification to assist in clinical diagnosis, BCI applications, and neuroergonomics.

Figure 1

Conceptual framework that features essential AI-driven elements for schizophrenia diagnosis and treatment and prognosis. The diagram combines multimodal data analysis with deep learning and clinical support features to support early detection and individualized treatment alongside proactive relapse prevention.

Figure 4

Workflow of EEG-based schizophrenia relapse prediction using a CNN-Transformer hybrid model.

Figure 5

Comparison between Proactive Relapse Detection (presented in this paper) and Post-Relapse Analysis in schizophrenia care. In the left column, it emphasizes the proactive–continuous monitoring, alerts (immediate and predictive), insights, and adjustments to prevent a fall. The column on the right pertains to analytical approaches following relapse events—review of historical records, outcome prediction, and statistical modeling. Abstract: A framework for continual patient feedback and outcome monitoring. Message: The framework combines real-time engagement with retrospective analysis in order to improve clinical decisions and patient outcomes.

Figure 6

Confusion matrices and precision analysis for assessing classification performance. (a) Confusion matrix of the classification results of the proposed model which has high MCC (MCC = 0.9318). (b) A comparative bar chart of Precision and PI-Score metrics pre and post model optimization. (c) Confusion matrix for the MultiViT model indicating the most notable misclassification. (d) Confusion matrix of the proposed model. The results show the higher predictive performance of the proposed model, with fewer false positives and negatives.

Figure 7

Performance validation using EEG data of the proposed schizpohrenia relapse prediction framework from different aspects. (a) The ROC curve of high classification performance. (b) The PR (Precision–Recall) curve represents the relationship between precision with respect to recall. (c) Calibration plot of predicted vs. observed probabilities. (d) Predicted probability histogram of schizophrenia classification. (e) Bar comparison of accuracy, precision, recall, and F1 for different models. (f) Comparison of sensitivities and specificities for different techniques, whereby better performance is exhibited by the proposed method.