This article provides research on sleep apnoea. Sleep apnoea is a capable for suspending breath or frequently pausing in period of deep sleep. This symptoms may leads to an unappropriate death that makes it a critical sleeping disorder. Periods of apnoea generally lasts for five seconds or hardly a minute which affects the sleeping pattern due to breathing. This probably happens five times of an hour or even more. Obstructive sleep apnoea (OSA),central sleep apnoea (CSA) and mixed/complex sleep apnoea(MSA) are common three types of apnoea, where mixed/complex sleep apnoea is combination of other two apnoea. Airway obstruction is caused in OSA, while in CSA airway is not blocked, but the brain dosn’t sends proper signals to the muscles that cause instability of the respiratory center. The study includes the sleep disorders, types, cause, signs and symptoms and methods of Sleep Apnoea. Considering the study; it is very much required to detection of sleep apnoea using noninvasive techniques. Machine learning algorithms based detection of sleep apnoea is a feasible solution which provides more than 90% accuracy. The study surveys the similar techniques based on machine learning.
M. K. Moridani, M. Heydar, S. J. Behnam, “A Reliable Algorithm Based on Combination of EMG, ECG and EEG Signals for Sleep Apnoea Detection”, 978-1-7281-0872-8/19/$31.00 ©2019 IEEE
S.Arulvallal, Snekhalatha.U and Rajalakshmi.T, “Design and Development of Wearable Device for Continuous Monitoring of Sleep APNOEA Disorder” 978-1-5386-7595-3/19/$31.00 ©2019 IEEE
G. C. Gutiérrez-Tobal, D. Álvarez, A. Crespo, F.Campo, and R. Hornero, “Evaluation of Machine-Learning Approaches to Estimate Sleep Apnoea Severity from at-Home Oximetry Recordings” 2168-2194 (c) 2018 IEEE.
Sinaakbarian, Ghazalehdelfi, Kaiyinzhu, Azadehyadollahi, Andbabaktaati, “Automated Non-Contact Detection of Head and Body Positions During Sleep”, 2169-3536 ©2019 IEEE
Yeongjunjeon and soon jukang, “Wearable Sleepcare Kit: Analysis and Prevention of Sleep Apnoea Symptoms in Real-Time”, 2169-3536© 2019 IEEE
KyeonghyeGuk, G.Han, J.Lim, K.Jeong, T.Kang, E.Lim and J.Jung, “Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare”, nano materials 29 May 2019
Sheikh shanawaz, et al., “A Review of Obstructive Sleep Apnoea Detection Approaches”, IEEE Journal of Biomedical and Health Informatics, pp. 1-1, 2168-2194 (c) 2018 IEEE.
Khandoker, A.H. and M. Palaniswami “Modeling respiratory movement signals during central and obstructive sleep apnoea events using electrocardiogram”,Annals of biomedical engineering, 39(2), pp. 801-811.20112010 Biomedical Engineering Society.February 2011
Necmettin SEZG_IN, “EMG classification in obstructive sleep apnoea syndrome and periodic limb movement syndrome patients by using wavelet packet transform and extreme learning machine”, Turk J ElecEng& Comp Sci, 23: pp. 873 – 884, 2015.
F.Kawana et al. “Automatic EEG arousal detection for sleep apnoea syndrome”, Biomedical Signal Processing and Control 4(4), pp. 329-337, 2009 Elsevier.
This work is licensed under a Creative Commons Attribution 4.0 International License.
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.
Submission of the manuscript represents that the manuscript has not been published previously and is not considered for publication elsewhere.