Multimodal Digital Phenotyping for Early Prediction of Manic Episodes Through Keystroke Dynamics and Circadian Pattern Analysis

Abstract Manic episodes in bipolar disorder are characterized by acute behavioral escalation requiring early intervention. This research proposes a multimodal digital phenotyping framework integrating keystroke dynamics with circadian rhythm features to forecast manic episodes 3-7 days prior to clinical onset. The system leverages a hybrid architecture of temporal convolutional and recurrent neural networks with personalized adaptation. It generates risk predictions and clinically actionable alerts while ensuring user privacy through strict on-device processing and data encapsulation. This framework addresses a critical gap in mental health-care: providing passive, unobtrusive monitoring to detect pre-onset behavioral signatures within a clinically actionable window.