Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO₂ signals

References

• Stöwhas A-C, Lichtblau M, Bloch KE. [Obstructive sleep apnea syndrome]. Praxis. 2019;108(2):111–117.
   PubMedGoogle Scholar
• Berry RB, Budhiraja R, Gottlieb DJ, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2012;8(5):597–619.
   PubMed Web of Science ®Google Scholar
• Leppänen T, Kulkas A, Mervaala E, et al. Increase in body mass index decreases duration of apneas and hypopneas in obstructive sleep apnea. Respir Care. 2019;64(1):77–84.
   PubMed Web of Science ®Google Scholar
• Shamim-Uzzaman QA, Singh S, Chowdhuri S. Hypopnea definitions, determinants and dilemmas: a focused review. Sleep Sci Pract. 2018;2(1):7.
   Google Scholar
• Benjafield A, Valentine K, Ayas N, et al. Global prevalence of obstructive sleep apnea in adults: estimation using currently available data. In B67. Risk and Prevalence of Sleep Disordered Breathing 2018 May. American Thoracic Society. p. A3962.
   Google Scholar
• Memon J, Manganaro SN. Obstructive sleep-disordered breathing (SDB). StatPearls [Internet]. 2020 Aug 14. Available from: https://www.ncbi.nlm.nih.gov/books/NBK441909/
   Google Scholar
• Iannella G, Magliulo G, Lo Iacono CA, et al. Positional Obstructive sleep apnea syndrome in elderly patients. Inter J Environ Res Public Health. 2020;17(3):1120.
   Google Scholar
• Nwosu N. Obstructive sleep apnoea: Important things to know. BDJ Student. 2019;26(3):40–42.
   Google Scholar
• Stearns JD, Stierer TL. Peri-operative identification of patients at risk for obstructive sleep apnea. Semin Anesth Perioper Med Pain. 2007;26(2):73–82.
   Google Scholar
• Thorey V, Hernandez AB, Arnal PJ, et al. AI vs Humans for the diagnosis of sleep apnea. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019; Berlin, Germany. p. 1596–1600.
   Google Scholar
• Almazaydeh L, Elleithy KM, Faezipour M. A new model for diagnosing sleep apnea through features extraction of the SpO2 signal. UB ScholarWorks. 2012. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/591
   Google Scholar
• Almazaydeh L, Elleithy K, Faezipour M. Obstructive sleep apnea detection using SVM-based classification of ECG signal features. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE; 2012 Aug 28-Sept 1. San Diego, CA: IEEE. p. 4938–4941.
   Google Scholar
• Martín-González S, Navarro-Mesa JL, Juliá-Serdá G, et al. Heart rate variability feature selection in the presence of sleep apnea: an expert system for the characterization and detection of the disorder. Comput Biol Med. 2017;91:47–58.
   PubMed Web of Science ®Google Scholar
• Lin Y-Y, Wu H-T, Hsu C-A, et al. Sleep apnea detection based on thoracic and abdominal movement signals of wearable Piezo-Electric bands. IEEE J Biomed Health Inform. 2016;21(6):1533–1545.
   PubMed Web of Science ®Google Scholar
• Ravelo-García A, Kraemer J, Navarro-Mesa J, et al. Oxygen saturation and RR intervals feature selection for sleep apnea detection. Entropy. 2015;17(5):2932–2957.
   Web of Science ®Google Scholar
• Al-Ratrout S, Hossen A. Support vector machine of wavelet packet spectral features for identification of obstructive sleep apnea. 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE). 2018. p. 380–3.
   Google Scholar
• M.S J, Jerusalem college of Engineering, Robin CRR. Fuzzy based expert system for sleep apnea diagnosis. IJETT. 2016;35(12):555–558.
   Google Scholar
• Mostafa S, Carvalho J, Morgado-Dias F, et al. Optimization of sleep apnea detection using SpO2 and ANN. XXVI International Conference on Information, Communication and Automation Technologies (ICAT). IEEE. 2017 Oct 26–28. Sarajevo, Bosnia and Herzegovina. p. 1–6.
   Google Scholar
• Alvarez D, Hornero R, Marcos J, et al. Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf. 2009. 2009:400–3.
   Google Scholar
• Punjabi M, Prabhu S. An ANN-Based detection of obstructive sleep apnea from simultaneous ECG and SpO 2 recordings. In International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 May 16. Palladam, India: Springer, Cham. p. 603–613.
   Google Scholar
• Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32(3):230–236.
   PubMed Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman J. Springer series in statistics the elements of statistical learning. Math Intell. 2009;27(2):83–85.
   Google Scholar
• Bawa K, Sabherwal P. R-Peak detection by modified Pan-Tompkins algorithm. Int J Adv Res Technol. 2014;3(5):30–33.
   Google Scholar
• Durgabai RPL. Feature selection using ReliefF algorithm. Ijarcce. 2014;3(10):8215–8218.
   Google Scholar
• Khandoker AH, Palaniswami M, Karmakar CK. Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings. IEEE Trans Inf Technol Biomed. 2009;13(1):37–48.
   PubMedGoogle Scholar
• Almazaydeh L, Elleithy K, Faezipour M. Detection of obstructive sleep apnea through ECG signal features. 2012 IEEE International Conference on Electro/Information Technology, 2012 May 6-8. pp. 1–6. Indianapolis, IN: IEEE.
   Google Scholar
• Xie B, Minn H. Real-time sleep apnea detection by classifier combination. IEEE Trans Inf Technol Biomed. 2012;16(3):469–477.
   PubMedGoogle Scholar
• Banluesombatkul N, Rakthanmanon T, Wilaiprasitporn T. Single channel ECG for obstructive sleep apnea severity detection using a deep learning approach. TENCON 2018 – 2018 IEEE Region 10 Conference, IEEE. 2018 Oct 28-31. pp. 2011–2016, Jeju, Korea (South).
   Google Scholar