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AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks
Prajoona Valsalan1, Mohammad Maroof Siddiqui1
1Department of Electrical and Computer Engineering, Dhofar University, Salalah 211, Oman.
Clocks & Sleep
|May 27, 2026
Summary
This study introduces a novel framework for secure sleep monitoring, integrating AI for sleep staging and anomaly detection. It achieves high accuracy and low latency, promising real-time, edge-deployable digital sleep health solutions.
Area of Science:
- Digital health and cybersecurity
- Artificial intelligence in medicine
- Sleep science and chronobiology
Background:
- Sleep disorders pose a growing global health challenge, linked to numerous chronic diseases.
- Wearable sensors and the Internet of Medical Things (IoMT) enable continuous, home-based sleep monitoring.
- Current systems face cybersecurity risks due to sensitive data transmission, with separate approaches for signal analysis and intrusion detection.
Purpose of the Study:
- To address the gap in integrated architectures for sleep monitoring that balance physiological modeling accuracy with communication security.
- To develop a unified framework for simultaneous sleep-stage classification and network anomaly detection.
- To evaluate the performance of a novel AI-driven approach for secure and efficient digital sleep health ecosystems.
Main Methods:
- A hybrid framework combining Convolutional Neural Networks (CNN) for spatial features, Bidirectional Long Short-Term Memory (BiLSTM) for temporal patterns, and Random Forest for classification.
- A dual-task learning approach optimizing both sleep-stage prediction and network anomaly detection.
- Multi-objective optimization for performance and security in Sleep Health Internet of Things (S-HIoT) systems.
Main Results:
- Achieved high accuracy: 99.8% for sleep staging and 98.6% for anomaly detection on public datasets (Sleep-EDF, CICIoMT2024).
- Demonstrated low inference latency (<45 ms), suitable for real-time deployment on edge devices.
- Validated a comprehensive framework for secure, intelligent, and clinically robust digital sleep health.
Conclusions:
- The proposed integrated framework effectively bridges sleep signal modeling with cybersecurity mechanisms.
- This approach offers a promising solution for secure, real-time digital sleep health monitoring.
- Future research should explore explainable AI, federated learning, and edge optimization for enhanced S-HIoT systems.
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