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Izvorni znanstveni članak
https://doi.org/10.31803/tg-20191104191722

Sleep apnea detection using deep learning

Hnin Thiri Chaw ; Dept of Computer Engineering, Prince of Songkla University, 90112 Hat Yai, Thailand
Sinchai Kamolphiwong ; Dept of Computer Engineering, Prince of Songkla University, 90112 Hat Yai, Thailand
Krongthong Wongsritrang ; Dept. of Otolaryngology Head Neck Surgery, Songklanagrind Hospital, 90112 Hat Yai, Thailand

Puni tekst: engleski, pdf (2 MB) str. 261-266 preuzimanja: 410* citiraj
APA 6th Edition
Chaw, H.T., Kamolphiwong, S. i Wongsritrang, K. (2019). Sleep apnea detection using deep learning. Tehnički glasnik, 13 (4), 261-266. https://doi.org/10.31803/tg-20191104191722
MLA 8th Edition
Chaw, Hnin Thiri, et al. "Sleep apnea detection using deep learning." Tehnički glasnik, vol. 13, br. 4, 2019, str. 261-266. https://doi.org/10.31803/tg-20191104191722. Citirano 22.06.2021.
Chicago 17th Edition
Chaw, Hnin Thiri, Sinchai Kamolphiwong i Krongthong Wongsritrang. "Sleep apnea detection using deep learning." Tehnički glasnik 13, br. 4 (2019): 261-266. https://doi.org/10.31803/tg-20191104191722
Harvard
Chaw, H.T., Kamolphiwong, S., i Wongsritrang, K. (2019). 'Sleep apnea detection using deep learning', Tehnički glasnik, 13(4), str. 261-266. https://doi.org/10.31803/tg-20191104191722
Vancouver
Chaw HT, Kamolphiwong S, Wongsritrang K. Sleep apnea detection using deep learning. Tehnički glasnik [Internet]. 2019 [pristupljeno 22.06.2021.];13(4):261-266. https://doi.org/10.31803/tg-20191104191722
IEEE
H.T. Chaw, S. Kamolphiwong i K. Wongsritrang, "Sleep apnea detection using deep learning", Tehnički glasnik, vol.13, br. 4, str. 261-266, 2019. [Online]. https://doi.org/10.31803/tg-20191104191722

Sažetak
Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.

Ključne riječi
continuous single bio-parameter recording; deep convolutional neural network; deep learning; type 4 sleep study; portable sleep apnea detection

Hrčak ID: 229492

URI
https://hrcak.srce.hr/229492

Posjeta: 718 *