Comparative Analysis of Vision Transformer, Convolutional, and Hybrid Architectures for Mental Health Classification Using Actigraphy-Derived Images

2025-11-27

AI Summary

This study addresses the automatic classification of psychiatric disorders—specifically major depressive disorder and schizophrenia—versus healthy controls using wrist-worn actigraphy signals. We propose a deep learning framework that converts raw actigraphy time-series data into image-like representations for classification. We systematically evaluate three architectures—VGG16 (CNN), ViT-B/16 (pure Transformer), and CoAtNet-Tiny (hybrid CNN-Transformer)—under small-sample and class-imbalanced conditions, employing three-fold cross-validation. Results demonstrate that CoAtNet-Tiny achieves significantly superior performance: highest mean accuracy and optimal F1-score, precision, and recall for both clinical classes, indicating enhanced robustness and discriminative capacity. To our knowledge, this is the first work to empirically validate the advantages of hybrid vision architectures for image-based analysis of physiological signals. The proposed end-to-end solution offers high efficiency and improved interpretability, making it particularly suitable for resource-constrained mental health screening applications.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Video Understanding & Activity Analysis
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This work examines how three different image-based methods, VGG16, ViT-B/16, and CoAtNet-Tiny, perform in identifying depression, schizophrenia, and healthy controls using daily actigraphy records. Wrist-worn activity signals from the Psykose and Depresjon datasets were converted into 30 by 48 images and evaluated through a three-fold subject-wise split. Although all methods fitted the training data well, their behaviour on unseen data differed. VGG16 improved steadily but often settled at lower accuracy. ViT-B/16 reached strong results in some runs, but its performance shifted noticeably from fold to fold. CoAtNet-Tiny stood out as the most reliable, recording the highest average accuracy and the most stable curves across folds. It also produced the strongest precision, recall, and F1-scores, particularly for the underrepresented depression and schizophrenia classes. Overall, the findings indicate that CoAtNet-Tiny performed most consistently on the actigraphy images, while VGG16 and ViT-B/16 yielded mixed results. These observations suggest that certain hybrid designs may be especially suited for mental-health work that relies on actigraphy-derived images.

Problem

• Evaluating vision transformer, convolutional, and hybrid models for mental health classification
• Comparing model performance on actigraphy-derived images for depression and schizophrenia detection
• Identifying the most reliable architecture for consistent and accurate mental health assessment

Innovation

• CoAtNet-Tiny hybrid architecture for actigraphy image classification
• Vision Transformer and CNN models compared for mental health detection
• Hybrid designs outperform pure CNN and Transformer in stability