Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors

Yeong Jun Jeon; Kuk Ho Heo; Soon Ju Kang

doi:10.1109/PerComWorkshops48775.2020.9156119

Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors

Yeong Jun Jeon
, Kuk Ho Heo
, Soon Ju Kang

Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

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.

Original language	English
Title of host publication	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728147161
DOIs	https://doi.org/10.1109/PerComWorkshops48775.2020.9156119
State	Published - Mar 2020
Event	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 - Austin, United States Duration: 23 Mar 2020 → 27 Mar 2020

Publication series

Name	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Conference

Conference	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Country/Territory	United States
City	Austin
Period	23/03/20 → 27/03/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

ANN
GNB
Healthcare
KNN
Machine Learning
Real-Time
Sleep Apnea
Wearable Device

Access to Document

10.1109/PerComWorkshops48775.2020.9156119

Cite this

Jeon, Y. J., Heo, K. H., & Kang, S. J. (2020). Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 Article 9156119 (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PerComWorkshops48775.2020.9156119

Jeon, Yeong Jun ; Heo, Kuk Ho ; Kang, Soon Ju. / Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020).

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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",

}

Jeon, YJ, Heo, KH & Kang, SJ 2020, Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors. in 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020., 9156119, 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, United States, 23/03/20. https://doi.org/10.1109/PerComWorkshops48775.2020.9156119

Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors. / Jeon, Yeong Jun; Heo, Kuk Ho; Kang, Soon Ju.
2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9156119 (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors

AU - Jeon, Yeong Jun

AU - Heo, Kuk Ho

AU - Kang, Soon Ju

PY - 2020/3

Y1 - 2020/3

N2 - 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.

AB - 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.

KW - ANN

KW - GNB

KW - Healthcare

KW - KNN

KW - Machine Learning

KW - Real-Time

KW - Sleep Apnea

KW - Wearable Device

UR - https://www.scopus.com/pages/publications/85091966346

U2 - 10.1109/PerComWorkshops48775.2020.9156119

DO - 10.1109/PerComWorkshops48775.2020.9156119

M3 - Conference contribution

AN - SCOPUS:85091966346

T3 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

BT - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Y2 - 23 March 2020 through 27 March 2020

ER -

Jeon YJ, Heo KH, Kang SJ. Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9156119. (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020). doi: 10.1109/PerComWorkshops48775.2020.9156119

Real-Time Sleep Apnea Diagnosis Method Using Wearable Device without External Sensors

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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