Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals

• Autorzy: Tripathy R. K.

Identyfikatory

DOI

10.1016/j.bbe.2017.11.003

• Języki publikacji: EN

Abstrakty

EN

Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as 'linear', 'polynomial', 'radial basis function (RBF)' and 'cosine wavelet kernel' is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals.

Słowa kluczowe

EN

sleep apnea; HRV signal; EDR signal; Fourier decomposition method; fuzzy entropy; kernel extreme learning machine

PL

bezdech senny; sygnał HRV; sygnał EDR; entropia rozmyta

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 1
• Strony: 136--144
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Tripathy R. K.

• Faculty of Engineering and Technology (ITER), Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar 751030, India

Bibliografia

• [1] Faust O, Acharya UR, Ng E, Fujita H. A review of ECG-based diagnosis support systems for obstructive sleep apnea. J Mech Med Biol 2016;16(01):1640004.
• [2] Gross JB, Bachenberg KL, Benumof JL, Caplan RA, Connis RT, Coté CJ, et al. Practice guidelines for the perioperative management of patients with obstructive sleep apnea: a report by the American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Anesthesiology 2006;104(5):1081.
• [3] Force AOSAT, of Sleep Medicine AA, et al. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med 2009;5 (3):263.
• [4] Mendez MO, Bianchi AM, 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.
• [5] Acharya UR, Chua EC-P, Faust O, Lim T-C, Lim LFB. Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters. Physiol Meas 2011;32(3):287.
• [6] de Chazal P, Reilly R, Heneghan C. Automatic sleep apnoea detection using measures of amplitude and heart rate variability from the electrocardiogram. 16th international conference on Pattern recognition, 2002. Proceedings, vol. 1. IEEE; 2002. p. 775–8.
• [7] Sadr N, de Chazal P. Comparing ECG derived respiratory signals and chest respiratory signal for the detection of obstructive sleep apnoea. Computing in cardiology conference (CinC), 2016. IEEE; 2016. p. 1029–32.
• [8] Al-Angari HM, Sahakian AV. Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 2007;54(10):1900–4.
• [9] De Chazal P, Heneghan C, Sheridan E. Apparatus for detecting sleep apnea using electrocardiogram signals, Apr. 11, 2006. US Patent 7,025,729.
• [10] Sadr N, de Chazal P. A comparison of obstructive sleep apnoea detection using three different ECG derived respiration algorithms. Computing in cardiology conference (CinC), 2015. IEEE; 2015. p. 301–4.
• [11] De Chazal P, Heneghan C, Sheridan E, Reilly R, Nolan P, O'Malley M. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans Biomed Eng 2003;50(6):686–96.
• [12] 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.
• [13] Penzel T, McNames J, De Chazal P, Raymond B, Murray A, Moody G. Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med Biol Eng Comput 2002;40(4):402–7.
• [14] Shouldice RB, O'brien LM, O'brien C, de Chazal P, Gozal D, Heneghan C. Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features. Sleep 2004;27(4):784–92.
• [15] de Chazal P, Penzel T, Heneghan C. Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram. Physiol Meas 2004;25(4):967.
• [16] Roche F, Gaspoz J-M, Minini P, Pichot V, Duverney D, Costes F, et al. Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation 1999;100 (13):1411–5.
• [17] Roche F, Duverney D, Court-Fortune I, Pichot V, Costes F, Lacour J-R, et al. Cardiac interbeat interval increment for the identification of obstructive sleep apnea. Pacing Clin Electrophysiol 2002;25(8):1192–9.
• [18] Roche F, Pichot V, Sforza E, Duverney D, Costes F, Garet M, et al. Predicting sleep apnoea syndrome from heart period: a time-frequency wavelet analysis. Eur Respir J 2003;22 (6):937–42.
• [19] Thomas RJ, Mietus JE, Peng C-K, Goldberger AL. An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. Sleep 2005;28 (9):1151–61.
• [20] Chua C-P, Heneghan C. Changes in cardiopulmonary coupling during sleep onset. International conference on Biomedical and pharmaceutical engineering, 2006. ICBPE 2006. IEEE; 2006. p. 137–41.
• [21] Liu D, Yang X, Wang G, Ma J, Liu Y, Peng C-K, et al. HHT based cardiopulmonary coupling analysis for sleep apnea detection. Sleep Med 2012;13(5):503–9.
• [22] Singh P, Joshi SD, Patney RK, Saha K. The Fourier decomposition method for nonlinear and non-stationary time series analysis. Proc R Soc A 2017;473:20160871.
• [23] Singh P, Pachori RB. Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms. J Mech Med Biol 2017;1740002.
• [24] Penzel T, Moody GB, Mark RG, Goldberger AL, Peter JH. The apnea-ECG database. Computers in cardiology 2000. IEEE; 2000. p. 255–8.
• [25] Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng 1985;(3):230–6.
• [26] Tripathy R, Sharma L, Dandapat S. Detection of shockable ventricular arrhythmia using variational mode decomposition. J Med Syst 2016;40(4):79.
• [27] Langley P, Bowers EJ, Murray A. Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features: application to ECG-derived respiration. IEEE Trans Biomed Eng 2010;57(4):821–9.
• [28] Singh P. Time-frequency analysis via the Fourier representation. ArXiv:1604.04992, 2016.
• [29] Dantas EM, Sant'Anna ML, Andre ao RV, Gonçalves CP, Morra EA, Baldo MP, et al. Spectral analysis of heart rate variability with the autoregressive method: what model order to choose? Comput Biol Med 2012;42(2):164–70.
• [30] Chen W, Wang Z, Xie H, Yu W. Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans Neural Syst Rehabil Eng 2007;15(2):266–72.
• [31] Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 2012;42(2):513–29.
• [32] Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 2014;6(3):376–90.
• [33] Li B, Rong X, Li Y. An improved kernel based extreme learning machine for robot execution failures. Sci World J 2014;2014.
• [34] Bishop CM. Pattern recognition and machine learning. Springer; 2006.
• [35] Farrar JT, Portenoy RK, Berlin JA, Kinman JL, Strom BL. Defining the clinically important difference in pain outcome measures. Pain 2000;88(3):287–94.
• [36] Reynolds EB, Seda G, Ware J, Vinik AI, Risk MR, Fishback NF. Autonomic function in sleep apnea patients: increased heart rate variability except during REM sleep in obese patients. Sleep Breath 2007;11(1):53–60.
• [37] de Chazal P, Sadr N. Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters. 2016 IEEE 38th annual international conference of the Engineering in Medicine and Biology Society (EMBC). IEEE; 2016. p. 3203–6.
• [38] Tripathy RK, Dandapat S. Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features. Healthc Technol Lett 2017;4(2):57–63.
• [39] Goshvarpour A, Goshvarpour A. Comparison of higher order spectra in heart rate signals during two techniques of meditation: Chi and Kundalini meditation. Cogn Neurodynamics 2013;7(1):39–46.
• [40] Noviyanto A, Isa SM, Wasito I, Arymurthy AM, et al. Selecting features of single lead ECG signal for automatic sleep stages classification using correlation-based feature subset selection. Int J Comput Sci Issues 2011;8(1):1178–81.
• [41] Acharya UR, Fujita H, Oh SL, Raghavendra U, Tan JH, Adam M, et al. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Gen Comput Syst 2017.
• [42] Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 2017;415:190–8.
• [43] Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 2017;405:81–90.
• [44] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017.
• [45] Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl-Based Syst 2017.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).