@inproceedings{2f7e3450176a44569ea73544447a28ac,
title = "Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors",
abstract = "Currently the diagnosis of sleep apnea is performed mainly in hospital by polysomnography. However, obstructive sleep apnea depend on various factors such as daily life pattern, sleep environment, and posture. Therefore, there is a need for a real-time wearable system that detects sleep apnea which is easy to use. In this paper, we suggest the sleep care system that can predict sleep apnea conveniently whenever wherever. We measured the respiration, SpO2, heartrate, and 3-ACC signals of sleep apnea patients using wearable device. We measured the respiration and SpO2 of patients to judge the levels of sleep apnea. Based on the measurement, we analyzed the heartrate and 3-ACC signals with various machine learning algorithms to determine if sleep apnea correlates with the measurement. As a result of this study, in realtime (640μs), we can diagnosis sleep apnea with 95\% accuracy by only analyzing heartrate and 3-ACC signals in a typical smart watch without external sensors.",
keywords = "ANN, GNB, Healthcare, KNN, Machine Learning, Real-Time, Sleep Apnea, Wearable Device",
author = "Jeon, \{Yeong Jun\} and Heo, \{Kuk Ho\} and Kang, \{Soon Ju\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 ; Conference date: 23-03-2020 Through 27-03-2020",
year = "2020",
month = mar,
doi = "10.1109/PerComWorkshops48775.2020.9156119",
language = "English",
series = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",
address = "United States",
}
