Di Credico Andrea, Perpetuini David, Izzicupo Pascal, Gaggi Giulia, Mammarella Nicola, Di Domenico Alberto, Palumbo Rocco, La Malva Pasquale, Cardone Daniela, Merla Arcangelo, Ghinassi Barbara, Di Baldassarre Angela
Department of Medicine and Aging Sciences, "G. D'Annunzio" University of Chieti-Pescara, 66100 Chieti, Italy.
UdA-TechLab, "G. D'Annunzio" University of Chieti-Pescara, 66100 Chieti, Italy.
Clocks Sleep. 2024 Jul 23;6(3):322-337. doi: 10.3390/clockssleep6030023.
Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.
睡眠质量（SQ）是整体健康的一个关键方面。睡眠质量差可能会导致认知障碍、情绪紊乱以及慢性病风险增加。因此，评估睡眠质量有助于识别有风险的个体并制定有效的干预措施。即使在清醒状态下，睡眠质量也已被证明会影响心率变异性（HRV）和皮肤温度。从这个角度来看，使用可穿戴设备和非接触技术持续监测心率和皮肤温度非常适合评估客观睡眠质量。然而，缺乏关于建立清醒状态下评估的心率变异性和皮肤温度指标之间关系的模型来预测睡眠质量的研究。本研究旨在基于心率变异性和皮肤温度开发机器学习模型，以估计匹兹堡睡眠质量指数（PSQI）所评估的睡眠质量。心率变异性通过可穿戴传感器测量，面部皮肤温度通过红外热成像测量。开发了基于单峰和多峰心率变异性及皮肤温度的分类模型。应用于多峰心率变异性和皮肤温度的支持向量机实现了最佳分类准确率，为83.4%。本研究可为采用可穿戴和非接触技术监测睡眠质量以用于人体工程学应用铺平道路。所提出的方法通过实现比现有最先进方法更高的分类准确率显著推进了该领域。我们的多峰方法利用了心率变异性和皮肤温度指标的协同效应，从而对睡眠质量提供了更全面的评估。诸如83.4%分类准确率这样的定量性能指标强调了我们的方法在使用清醒状态下的非侵入性测量准确预测睡眠质量方面的稳健性和潜力。
