This project aims to develop an intelligent system for monitoring and diagnosing sleep disorders through the integration of Internet of Medical Things (IoMT), Machine Learning (ML), and Blockchain technologies. Sleep disorders are often underdiagnosed due to the lack of continuous, objective, and secure monitoring solutions. By utilizing UK Biobank’s physiological and health data, this research will explore the relationship between sleep patterns and various health indicators to enable accurate, data-driven diagnosis and management. The study seeks to determine how multimodal physiological signals such as heart rate variability (HRV), blood oxygen saturation (SpO!), and electroencephalogram (EEG) can be used to detect and predict sleep-related abnormalities. Advanced hybrid ML models, including Graph Attention Networks (GAT), Temporal Graph Networks (TGN), and Neural Ordinary Differential Equations (Neural ODEs), will be developed to improve predictive accuracy and interpretability. Moreover, a blockchain-based data framework will be implemented to ensure the privacy, integrity, and interoperability of sensitive medical data. The project will result in a personalized, AI-driven platform capable of providing individualized recommendations for sleep improvement and health management. By combining real-time physiological monitoring, advanced machine learning, and secure data storage, this research will contribute to the development of innovative, reliable, and ethical digital healthcare systems. The scientific rationale of this work lies in its ability to transform sleep health assessment into a continuous, intelligent, and privacy-preserving process, enabling better diagnosis, treatment, and prevention of sleep disorders across diverse populations.
