TY - GEN
T1 - A Portable Monitoring System with Automatic Event Detection for Sleep Apnea Level-IV Evaluation
AU - Wu, Jhao Cheng
AU - Wang, Chia Wei
AU - Huang, Yuan Hao
AU - Wu, Hau Tieng
AU - Huang, Po Chiun
AU - Lo, Yu Lun
N1 - Funding Information:
ACKNOWLEDGMENT Hau-tieng Wu acknowledges the support of Sloan Research Fellowships, FR-2015-65363. This work was supported by the Ministry of Science and Technology (MoST), Taiwan, under grant number MOST 104-2220-E-007-019.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/26
Y1 - 2018/4/26
N2 - To meet the demands on a comfortable screening, or even diagnostic, equipment without interfering with the sleep, this study develops a level IV portable system, equipped with two tri-axial accelerometers (TAA) measuring the thoracic and abdominal respiratory efforts, and one oximeter measuring the oxygen saturation (SpO2), to identify obstructive sleep apnea (OSA), central sleep apnea (CSA), and hypopnea (HYP) events. The prototype integrates all the hardware and software for physiological information extraction. In addition, an automatic event detection algorithm is proposed to reduce the labor-intensive work on scoring the events. Based on 63 subjects, with 80% data for training and 20% for validation, the classification accuracy of the apnea hypopnea-index (AHI) is 84.13%. The results indicate that the proposed algorithm has great potential to classify the severity of patients in clinical examinations for both the screening and the homecare purposes.
AB - To meet the demands on a comfortable screening, or even diagnostic, equipment without interfering with the sleep, this study develops a level IV portable system, equipped with two tri-axial accelerometers (TAA) measuring the thoracic and abdominal respiratory efforts, and one oximeter measuring the oxygen saturation (SpO2), to identify obstructive sleep apnea (OSA), central sleep apnea (CSA), and hypopnea (HYP) events. The prototype integrates all the hardware and software for physiological information extraction. In addition, an automatic event detection algorithm is proposed to reduce the labor-intensive work on scoring the events. Based on 63 subjects, with 80% data for training and 20% for validation, the classification accuracy of the apnea hypopnea-index (AHI) is 84.13%. The results indicate that the proposed algorithm has great potential to classify the severity of patients in clinical examinations for both the screening and the homecare purposes.
KW - automatic apnea/hypopnea event detection
KW - portable system
KW - respiratory effort detection
KW - sleep apnea hypopnea
UR - http://www.scopus.com/inward/record.url?scp=85057110912&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057110912&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2018.8351221
DO - 10.1109/ISCAS.2018.8351221
M3 - Conference contribution
AN - SCOPUS:85057110912
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Y2 - 27 May 2018 through 30 May 2018
ER -