Detection of melatonin-onset in real settings via wearable sensors and artificial intelligence. A pilot study
Detection of melatonin-onset in real settings via wearable sensors and artificial intelligence. A pilot study
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DOI：
10.1016/j.bspc.2020.102386
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发表时间：
2021-03-01
影响因子：
5.1
通讯作者：
Pecchia, L.
中科院分区：
文献类型：
--
作者：
Castaldo, R.;Chappell, M. J.;Pecchia, L.
昼夜节律调节大约24小时周期性的生理和行为过程。昼夜节律计时系统的改变可能导致心血管、代谢或神经系统疾病、癌症和睡眠障碍，以及生活质量的破坏。昼夜节律可以通过测量唾液、尿液或血液样本中激素的实验室测试来跟踪，这些样本是在受控环境中收集的。这些测试不适合于现实生活中的连续监测，昂贵且耗时，产生离散信息（即，每天几个值）并且需要受控的环境条件（例如，暴露于光可以改变样品）。因此，需要开发非侵入性的方法和工具来跟踪现实生活条件下的昼夜节律。CE认证）可穿戴传感器，连续两天连续测量ECG、皮肤体温和身体活动。每天最多取10个唾液样本，送到实验室测量褪黑激素，这是用来作为昼夜节律tracking.Results代理本文提出的结果表明，心率变异性（HRV）的措施，身体活动和皮肤温度发生显着变化后，褪黑激素的发病。本研究中提出的深度学习模型检测褪黑激素的发病率为71%，灵敏度为67%，特异性为75%，曲线下面积（AUC）为77%。目前的研究得出结论，深度学习可用于跟踪现实生活中的褪黑激素发病率，使用通过可穿戴和易于使用的传感器监测的生理和行为测量。
Circadian rhythms modulate physiological and behavioral processes of approximately 24-h periodicity. Alterations in the circadian timing system may lead to cardiovascular, metabolic or neurological diseases, cancers and sleep disorders, as well as to disruption of quality of life. Circadian rhythms can be tracked via laboratory tests measuring hormones in salivary, urinary or blood samples, which are collected in controlled environments. These tests are unsuitable for continuous monitoring in real-life, being expensive and time consuming, producing discrete information (i.e., few values per day) and requiring controlled environmental conditions (e.g., exposure to light can alter the samples). Thus, there is a need to develop non-invasive methods and tools to track circadian rhythms in real-life conditions.In this study, 10 healthy participants wore commercial medical-rated (i.e., CE-marked) wearable sensors, which continuously measured ECG, skin body temperature and physical activity for two consecutive days. Up to 10 salivary samples per day were taken and sent to a laboratory for measuring melatonin, which was used as proxy for circadian rhythm tracking.The results presented in this paper demonstrated that Heart Rate Variability (HRV) measures, physical activity and skin temperature changed significantly after the onset of melatonin. The deep-learning model presented in this study detected the onset of melatonin with 71 % accuracy, 67 % sensitivity, 75 % specificity and 77 % area under the curve (AUC).The current study concluded that deep learning could be used to track melatonin-onset in real-life, using physiological and behavioral measures monitored via wearable and easy-to-use sensors.