An automated method for sleep apnoea detection using HRV

References

• Ho ML, Brass SD. Obstructive sleep apnea. Neurol Int. 2011;3(3):e15.
   PubMedGoogle Scholar
• Dempsey JA, Veasey SC, Morgan BJ, et al. Pathophysiology of sleep apnea. Physiol Rev. 2010;90(1):47–112.
   PubMed Web of Science ®Google Scholar
• Institute of Medicine (US) Committee on Sleep Medicine and Research; Colten HR, Altevogt BM, editors. Sleep disorders and sleep deprivation: an unmet public health problem. Washington (DC): National Academies Press (US); 2006. 3, Extent and Health Consequences of Chronic Sleep Loss and Sleep Disorders. Available from: https://www.ncbi.nlm.nih.gov/books/NBK19961/
   Google Scholar
• Rundo JV, Downey R. 3rd. Polysomnography. Handb Clin Neurol. 2019;160:381–392. PMID: 31277862.
   PubMedGoogle Scholar
• Gharavi M, Zabihian S. Evaluation of apnea test in patients with brain death. Medical Journal of Mashhad University of Medical Sciences. 2007;50(97):295–300.
   Google Scholar
• Jin K, Bagui S. Apache spark SVM for predicting obstructive sleep apnea. BDCC. 2020;4(4):25.
   Web of Science ®Google Scholar
• Yılmaz B, Asyalı MH, Arıkan E, et al. Sleep stage and obstructive apneaic epoch classification using single-lead ECG. BioMed Eng Online. 2010;9(1):39.
   PubMed Web of Science ®Google Scholar
• Khandoker AH, Karmakar CK, Palaniswami M. Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea. Med Eng Phys. 2011;33(2):204–209. Mar9. Epub 2010 Oct 25. PMID: 20980188.
   PubMed Web of Science ®Google Scholar
• Acharya U,R, Bhat S, Faust O, et al. Nonlinear dynamics measures for automated EEG-Based sleep stage Detection. Eur Neurol. 2015;74(5-6):268–287. :
   PubMed Web of Science ®Google Scholar
• Aminian O, Sadeghniiat Haghighi KH, Shams Hosseini NS. Frequency of depression in patients who suffering from insomnia and obstructive sleep apnea syndrome diagnosed by nocturnal polysomnography. Occup Med. 2014; 6(2):76–83. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=405647
   Google Scholar
• Moridani MK, Anisi N, Eslami AH. Presenting a New Method to Obstructive Sleep Apnea Prediction Using Electrocardiogram Signal, The International Conference in New Research of Electrical Engineering and Computer Science, University of Tehran, Iran, 2015.
   Google Scholar
• Leelaarporn P, Wachiraphan P, Kaewlee T, et al. Sensor-Driven achieving of smart living: a review. IEEE Sensors J. 2021;21(9):10369–10391.
   Web of Science ®Google Scholar
• Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–20. PMID: 10851218.
   PubMed Web of Science ®Google Scholar
• Karimi Moridani A, Hoora Fakhrmoosavy S, Moridani MK. Vehicle detection and tracking in roadway traffic analysis using kalman filter and features. Int J Imaging Robotics. 2015; 15(2):45–52.
   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
• Moridani MK, Abdi Zadeh M, Shahiazar Mazraeh Z. An efficient automated algorithm for distinguishing normal and abnormal ECG signal. IRBM. 2019;40(6):332–340.
   Web of Science ®Google Scholar
• Song MH, Lee J, Cho SP, et al. Support vector machine based arrhythmia classification using reduced features. Int J Control Autom Syst. 2005;3(4):571–579.
   Web of Science ®Google Scholar
• Fatma GüLera N, Derya U Beylib E, GüLera I. " Recurrent neural network-based approach for detection of epileptiform patterns in EEG. Computer Biol Med. 2005;34:23–30.
   Google Scholar
• Subasia A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert System Appl. 2006;45:76–87.
   Google Scholar
• Moridani MK, Pouladian M. Detection ischemic episodes from electrocardiogram signal using wavelet transform. J Biomed Sci Eng. 2009;2(4):239–244.
   Google Scholar
• Mahmoodabadi SZ, Ahmadian A, Abolhasani MD, et al. ECG Feature Extraction Based onMultiresolution Wavelet Transform, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual ConferenceShanghai, China, 2005.
   Google Scholar
• Kandaswamy A, Kumar CS, Ramanathan P, et al. Neural classification of lung sounds using wavelet coefficients. Comput Phys Commun. 2004;34(6):523–553., September
   Google Scholar
• Nguyen LH, Holmes S. Ten quick tips for effective dimensionality reduction. PLoS Comput Biol. 2019;15(6):e1006907
   PubMed Web of Science ®Google Scholar
• Xu M, Fralick D, Zheng JZ, et al. The differences and similarities between Two-Sample T-Test and paired T-Test. Shanghai Arch Psychiatry. 2017;29(3):184–188.
   PubMedGoogle Scholar
• Jolliffe Ian T, Jorge C. Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A Math Phys Eng Sci. 2016;374(2065):20150202.
   Google Scholar
• Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl. 2021;80(5):8091–8126.
   PubMed Web of Science ®Google Scholar
• Karimi Moridani M, Marjani S. A review of the methods for sudden cardiac death detection. Int J Onl Eng. 2020; 16(09):137–158.
   Google Scholar
• Soleimanian Gharehchopogh F, Molany M, Mokri FD. Using artificial neural network in diagnosis of thyroid disease: a case study. Int J Comput Sci Appl. 2013; 3(4):49–61.
   Google Scholar
• Dunne RA, Wiley J, Inc S. A statistical approach to neural networks for pattern recognition. New Jersey: John Wiley and Sons Inc; 2007.
   Google Scholar
• Lorena A, de Carvalho A. Evolutionary tuning of SVM parameter values in multiclass problems. Neurocomputing. 2008;71(16-18):3326–3334.
   Web of Science ®Google Scholar
• Fauvel M, Chanussot J, Benediktsson J. A Combined Support Vector Machines Classification Based on Decision Fusion, 2006 IEEE International Symposium on Geoscience and Remote Sensing, USA, 2007.
   Google Scholar
• Zhao L, Lee S, Jeong S-P. Decision tree application to classification problems with boosting algorithm. Electronics. 2021;10(16):1903.
   Web of Science ®Google Scholar
• Zhang J, Tang Z, Gao J, et al. Automatic detection of obstructive sleep apnea events using a deep CNN-LSTM Model. Comput Intell Neurosci. 2021;2021:5594733 vol. Article ID 5594733, 10 pages,
   PubMed Web of Science ®Google Scholar
• Lin R, Lee R, Tseng C, et al. A new approach for identifying sleep apnea syndrome using wavelet transfrom and neural networks. Biomed Eng Appl Basis Commun. 2006;18(03):138–143.,
   Web of Science ®Google Scholar
• Pepin JL, Defaye P, Christophle-Boulard S, et al. Sleep apnea diagnosis using an ECG holter device including a nasal pressure (NP) recording: Validation of visual and automatic analysis of nasal pressure versus full polysomnography. Sleep Med. 2009;10(6):651–656.,
   PubMed Web of Science ®Google Scholar
• Yilmaz B, Asyali M, Arikan E, et al. Sleep stage and obstructive apneaic epoch classification using single-lead ECG. Biomed Eng Online. 2010;9:1–14.,
   PubMed Web of Science ®Google Scholar
• Xie B, Minn H. Real time sleep apnea detection by classifier combination. IEEE Trans Inform Technol Biomed. 2012;15:469–477.
   Google Scholar
• Gubbi J, Khandoker A, Palaniswami M. Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals. J Clin Monit Comput. 2012;26(1):1–11.
   PubMed Web of Science ®Google Scholar
• Thommandram A, Eklund JM, Mcgregor C. Detection of apnoea from respiratory time series data using clinically recognizable features and kNN classification, 35th Annual Internatioal Conference of IEEE Engineering in Medicine and Biology Society 2013. p. 5013–5016.
   Google Scholar
• Rachim VP, Li G, Chung W-Y. Sleep apnea classification using ECG-signal wavelet-PCA features. Biomed Mater Eng. 2014;24(6):2875–2882.
   PubMed Web of Science ®Google Scholar
• Hassan AR. Computer-aided obstructive sleep apnea detection using normal inverse gaussian parameters and adaptive boosting. Biomed Signal Process Control. 2016;29:22–30.
   Web of Science ®Google Scholar
• Hassan AR, Haque MA. An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting. Neurocomputing. 2017;235:122–130.
   Web of Science ®Google Scholar
• Banluesombatkul N, Rakthanmanon T, and T. Wilaiprasitporn “Single channel ECG for 0bstructive sleep apnea severity detection using a deep learning approach,” in Proceedings of the 2018 IEEE Region 10 Conference, p. 2011–2016. Jeju Island, Korea, October 2018.
   Google Scholar
• Urtnasan E, Park JU, Joo EY, et al. Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network. J Med Syst. 2018;42(6):104.
   PubMed Web of Science ®Google Scholar
• Tripathy RK. Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals. Biocybernet Biomed Eng/Polish Acad Sci. Inst Biocybernetics Biomed Eng. 2018;38(1):136–144.
   Web of Science ®Google Scholar
• Zarei A, Asl BM. Automatic detection of obstructive sleep apnea using wavelet transform and entropy-based features from single-lead ECG signal. IEEE J Biomed Health Inform. 2019;23(3):1011–1021.
   PubMed Web of Science ®Google Scholar
• Chazal P, Heneghan C, Sheridan E, et al. Automatic classification of sleep apnea epochs using the electrocardiogram. Proc. of Computers in Cadiology Conf. 2000;27:745–748.
   Google Scholar