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Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. However, the existing polysomnography method is not easily accessible, costly, and burdensome to patients, requiring specialized facilities and personnel. Here we report at-home portable wireless sleep sensors and wearable electronics with embedded machine learning and their applications in assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft all-integrated wearable platform offers natural sleep at places users prefer. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.
Kim et al. (Thu,) studied this question.