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Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

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Abstract

Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recognition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status, and race information which are known influence factors. Moreover, we developed a personalized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decision-making. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore offering a new solution for personalized sleep-based health monitoring.

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Funding

This work was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 676157.

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Authors and Affiliations

  1. School of Computer Science and Informatics, De Montfort University, Leicester, UK

    Sarah Fallmann & Liming Chen

  2. School of Computing, Ulster University, Belfast, UK

    Feng Chen

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  1. Sarah Fallmann
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  2. Liming Chen
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Correspondence to Sarah Fallmann.

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Fallmann, S., Chen, L. & Chen, F. Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction. Pers Ubiquit Comput 28, 363–383 (2024). https://doi.org/10.1007/s00779-020-01445-9

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