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© 2020 The Authors 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.

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Journal article


Biomedical Signal Processing and Control

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