A Hybrid Deep Learning Framework for Detecting Bipolar Disorder Through Persian Handwriting Patterns

Bipolar disorder (BD) remains a pervasive mental health challenge, demanding innovative diagnostic approaches beyond traditional, subjective assessments. This study pioneers a non-invasive handwriting-based diagnostic framework, leveraging the unique interplay between psychological states and motor expressions in writing. Our hybrid deep learning model, combining ResNet for intricate feature extraction and external attention mechanisms for global pattern analysis, achieves a remarkably high accuracy 99%, validated through Leave-One-Subject-Out (LOSO) cross-validation. Augmented with advanced data preprocessing and augmentation techniques, the framework adeptly addresses dataset imbalances and handwriting variability. For the first time, Persian handwriting serves as a medium, bridging cultural gaps in BD diagnostics. This work not only establishes handwriting as a transformative tool for mental health diagnostics but also sets the stage for accessible, scalable, and culturally adaptive solutions in global mental healthcare.