COGNITIVELSTM: A DEEP LEARNING FRAMEWORK FOR REALTIME PREDICTION OF MENTAL HEALTH EPISODES USING MULTIMODAL BIOSENSOR AND MOBILE HEALTH DATA
DOI: https://doi.org/10.65725/JCISE/1/1/004
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)
ISSN: 3107-8168
Volume 1 Issue 1, July 2025

Abstract
Mental health disorders represent a critical global health challenge, with significant societal and economic burdens exacerbated by limited access to timely interventions. While wearable biosensors and mobile health (mHealth) systems enable continuous monitoring of physiological and behavioral biomarkers, existing methods struggle to leverage this multimodal data for accurate episode prediction. This paper presents Cognitive LSTM—a novel deep learning architecture that integrates temporal patterns from  wearable-derived physiological signals (heart rate variability, electrodermal activity) with mHealth-reported behavioral data (sleep quality, social engagement, environmental stressors) to predict impending mental health episodes. The model employs attention-enhanced LSTM networks to learn latent interactions between heterogeneous data streams, achieving 89.3% prediction accuracy (F1-score: 0.87) in cross-validated trials with a 72-hour forecasting window. A federated learning implementation preserves user privacy by enabling decentralized model personalization without raw data sharing. Compared to conventional SVM-based approaches, CognitiveLSTM reduces false alarms by 34% through its dual-phase anomaly detection system. Clinical validation with 1,200 participants demonstrates its efficacy in enabling preemptive interventions, showing a 40% reduction in acute episode severity when alerts are acted upon. This framework pioneers a scalable solution for personalized mental healthcare, bridging the gap between continuous biosensing and actionable clinical insights..

Authors: Dr. Gowri K, Dr. Lavanya  M

Keywords: Effective Management; Environmental Factors; Personalized Predictions; Early Detection; Revolutionize Mental; Mental Health Disorders;