TY - JOUR
T1 - IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring
AU - Lin, Chin Teng
AU - Prasad, Mukesh
AU - Chung, Chia Hsin
AU - Puthal, Deepak
AU - El-Sayed, Hesham
AU - Sankar, Sharmi
AU - Wang, Yu Kai
AU - Singh, Jagendra
AU - Sangaiah, Arun Kumar
N1 - Funding Information:
This work was supported in part by the Australian Research Council under Grant DP150101645, in part by Central for Artificial Intelligence, UTS, Australia, in part by the U.S. Army Research Laboratory under Grant W911NF-10-2-0022 and Grant W911NF-10-D-0002/TO 0023, and in part by the Roadway, Transportation, and Traffic Safety Research Center, United Arab Emirates University, under Grant 31R058.
Publisher Copyright:
© 2013 IEEE.
PY - 2017/10/24
Y1 - 2017/10/24
N2 - Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-Term tracing and research of personal sleep monitoring at home.
AB - Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-Term tracing and research of personal sleep monitoring at home.
KW - Internet of Things
KW - Java
KW - Polysomnography (PSG)
KW - sleep monitoring
KW - wireless
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U2 - 10.1109/ACCESS.2017.2765702
DO - 10.1109/ACCESS.2017.2765702
M3 - Article
AN - SCOPUS:85032435870
VL - 6
SP - 405
EP - 414
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -