Deep learning-driven ADHD detection using high-resolution heart rate variability analysis

Diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) presents several challenges, especially due to its reliance on subjective judgment and observation of behavior. While these age-old practices remain in vogue, they don’t usually provide consistency or objectivity in early and correct identification. With increasing interest in physiological signals as digital biomarkers, this study examines the application of Heart Rate Variability data in supporting automated ADHD classification. The data used in this research integrated HRV based statistical features, cognitive performance scores, and psychological screening test scores of 81 subjects. After feature extraction and feature selection, 28 significant features were retained. Three deep learning architectures Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were used to compare their performance in identifying ADHD patterns from sequential physiological signals. Collectively, these results justify the use of ML-based classifiers in facilitating clinical decision-making in the ADHD diagnosis process. Moreover, given their compatibility with wearables, it could offer cost-effective scalable solutions for early detection and continuous monitoring of mental health.