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http://yadda.icm.edu.pl:443/baztech/element/bwmeta1.element.baztech-4d0e33e0-c1c8-4dd3-b5bf-279aa8f57dd0

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating

Autorzy Hassan, A. R.  Haque, M. A. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Automatic sleep apnea screening is important to alleviate the onus of the physicians of analyzing a large volume of data visually. Again, the push towards low-power, portable and wearable sleep quality monitoring systems necessitates the use of minimum number of recording channels to enhance battery life. So, there is a dire need of an automated apnea detection scheme based on single-lead electrocardiogram (ECG). Most of the existing works are based on multiple channels of physiological signals or yield poor performance. The effect of various classification models on algorithmic performance is also poorly explored. In the present work, we propose a statistical and spectral feature based sleep apnea identification scheme that utilizes single-lead ECG signals. Bootstrap aggregating is employed to perform classification. The efficacy of the selected features is demonstrated by intuitive, statistical and graphical analyses. Optimal choices of classifier parameters are also expounded. The performance of the proposed algorithm is evaluated for various classifiers. The performance of our method is also compared to that of the state-of-the-art ones. The proposed method yields accuracy, sensitivity and specificity of 85.97%, 84.14% and 86.83% respectively on a widely used benchmark data-set. Experimental findings backed by statistical and graphical analyses suggest that the proposed method performs better than the existing ones in terms of accuracy, sensitivity, specificity and computational cost.
Słowa kluczowe
PL bezdech senny   klasyfikacja statystyczna   cecha spektralna  
EN sleep apnea   classification   statistical features   spectral features   bagging  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 256--266
Opis fizyczny Bibliogr. 36 poz., rys., tab., wykr.
Twórcy
autor Hassan, A. R.
  • Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, 508/3, South Goran, Dhaka 1219, Bangladesh, ahnaf.hassan0@gmail.com
autor Haque, M. A.
  • Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
Bibliografia
[1] Baguet J-P, Barone-Rochette G, Tamisier R, Levy P, Pépin J-L. Mechanisms of cardiac dysfunction in obstructive sleep apnea. Nat Rev Cardiol 2012;9(12):679–88.
[2] Jin J, Sanchez-Sinencio E. A home sleep apnea screening device with time-domain signal processing and autonomous scoring capability. IEEE Trans Biomed Circuits Syst 2015;9(1):96–104.
[3] Maier C, Dickhaus H. Recurrence analysis of nocturnal heart rate in sleep apnea patients. Biomed Tech 2006;51(4):224–8.
[4] Khandoker A, Gubbi J, Palaniswami M. Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings. IEEE Trans Inf Technol Biomed 2009;13(6):1057–67.
[5] Mendez M, Bianchi A, Matteucci M, Cerutti S, Penzel T. Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Trans Biomed Eng 2009;56(12):2838–50.
[6] Khandoker A, Palaniswami M, Karmakar C. Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings. IEEE Trans Inf Technol Biomed 2009;13(1):37–48.
[7] Yildiz A, Akin M, Poyraz M. An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings. Expert Syst Appl 2011;38 (10):12880–9.
[8] Bsoul M, Minn H, Tamil L. Apnea medassist: Real-time sleep apnea monitor using single-lead ECG. IEEE Trans Inf Technol Biomed 2011;15(3):416–27.
[9] Xie B, Minn H. Real-time sleep apnea detection by classifier combination. IEEE Trans Inf Technol Biomed 2012;16(3): 469–77.
[10] Varon C, Caicedo A, Testelmans D, Buyse B, Huffel S. A novel algorithm for the automatic detection of sleep apnea from single-lead ECG. IEEE Trans Biomed Eng 2015;(99):1.
[11] Al-Angari H, Sahakian A. Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier. IEEE Trans Inf Technol Biomed 2012;16 (3):463–8.
[12] Chen L, Zhang X, Song C. An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram. IEEE Trans Autom Sci Eng 2015;12 (1):106–15.
[13] Nguyen HD, Wilkins B, Cheng Q, Benjamin B. An online sleep apnea detection method based on recurrence quantification analysis. IEEE J Biomed Health Inf 2014;18 (4):1285–93.
[14] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(June (23)): e215–20.
[15] Hassan A, Bashar S, Bhuiyan M. On the classification of sleep states by means of statistical and spectral features from single channel electroencephalogram. International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2015. pp. 2238–43.
[16] Lerch A. An introduction to audio content analysis: applications in signal processing and music informatics. Wiley-IEEE Press; 2012.
[17] Peeters G. A large set of audio features for sound description (similarity and classification) in the CUIDADO project. Tech. Rep., IRCAM; 2004.
[18] Hassan AR, Bhuiyan MIH. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 2016;24:1–10.
[19] Hassan AR, Haque MA. Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain. TENCON 2015, IEEE Region 10 Conference. 2015. pp. 1–6.
[20] Hassan AR, Bashar SK, Bhuiyan MIH. Automatic classification of sleep stages from single-channel electroencephalogram. Annual IEEE India Conference (INDICON). 2015. pp. 1–6.
[21] Hassan AR, Haque MA. Computer-aided sleep apnea diagnosis from single-lead electrocardiogram using dual tree complex wavelet transform and spectral features. International Conference on Electrical and Electronic Engineering (ICEEE). 2015. pp. 1–4.
[22] Bashar S, Hassan A, Bhuiyan M. Identification of motor imagery movements from EEG signals using dual tree complex wavelet transform. International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2015. pp. 290–6.
[23] Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Progr Biomed 2015.
[24] Breiman L. Bagging predictors. Mach Learning 1996;24 (2):123–40.
[25] Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag 2006;6(3):21–45.
[26] Khandoker AH, Karmakar CK, Palaniswami M. Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings. Comput Biol Med 2009;39(3):88–96.
[27] Kesper K, Canisius S, Penzel T, Ploch T, Cassel W. ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern. Med Biol Eng Comput 2012;50 (2):135–44.
[28] Hassan AR. Automatic screening of obstructive sleep apnea from single-lead electrocardiogram. International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). 2015. pp. 1–6.
[29] Babaeizadeh S, White DP, Pittman SD, Zhou SH. Automatic detection and quantification of sleep apnea using heart rate variability. J Electrocardiol 2010;43(6):535–41.
[30] Marcos JV, Hornero R, Álvarez D, del Campo F, Zamarrón C. Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Med Eng Phys 2009;31(8):971–8.
[31] Burgos A, Goñi A, Illarramendi A, Bermudez J. Real-time detection of apneas on a PDA. IEEE Trans Inf Technol Biomed 2010;14(4):995–1002.
[32] Wolpert DH. The lack of a priori distinctions between learning algorithms. Neural Comput 1996;8(7):1341–90.
[33] Hassan AR. A comparative study of various classifiers for automated sleep apnea screening based on single-lead electrocardiogram. International Conference on Electrical and Electronic Engineering (ICEEE). 2015. pp. 1–4.
[34] Murphy KP. Machine learning: a probabilistic perspective. The MIT Press; 2012.
[35] Salisbury JI, Sun Y. Rapid screening test for sleep apnea using a nonlinear and nonstationary signal processing technique. Med Eng Phys 2006;29(3):336–43.
[36] Mendez MO, Corthout J, Huffel SV, Matteucci M, Penzel T, Cerutti S, et al. Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis. Physiol Meas 2010;31 (3):237–89.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-4d0e33e0-c1c8-4dd3-b5bf-279aa8f57dd0
Identyfikatory
DOI 10.1016/j.bbe.2015.11.003