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.

传统上，睡眠行为是通过多导睡眠监测仪来监测的，睡眠阶段模式是用于检测异常和诊断疾病的睡眠质量的关键标志。随着对个性化医疗保健的需求不断增长以及物联网的普及，趋势是在家庭中使用日常技术进行睡眠行为分析，从而有可能消除昂贵的医院内监测。在本文中，我们构思了一种多源数据挖掘方法，用于个性化的睡眠-唤醒模式识别，该方法使用生理数据和个人信息来促进细粒度检测。生理数据包括书法和心率变异性，而个人数据则利用性别，健康状况和种族信息等已知的影响因素。此外，我们开发了一种与睡眠唤醒方法相融合的个性化睡眠参数提取技术，可实现个性化决策，而非静态阈值。结果表明，所提出的方法提高了睡眠和唤醒阶段识别的准确性，因此为基于个性化睡眠的健康监测提供了新的解决方案。