Enhancing Obstructive Apnea Disease Detection Using Dual-Tree Complex Wavelet Transform-Based Features and the Hybrid “K-Means, Recursive Least-Squares” Learning for the Radial Basis Function Network

Javad Ostadieh, Mehdi Chehel Amirani, Morteza Valizadeh

DOI: 10.4103/jmss.JMSS_69_19


Background: The obstructive sleep apnea (OSA) detection has become a hot research topic because of the high risk of this disease. Aims and Objectives: In this paper, we tested some powerful and low computational signal processing techniques for this task and compared their results with the recent achievements in OSA detection. Methods: The Dual-tree complex wavelet transform (DTCWT) is used in this paper to extract feature coefficients. From these coefficients, eight non-linear features are extracted and then reduced by the Multi-cluster feature selection (MCFS) algorithm. The remaining features are applied to the hybrid “K-means, RLS” RBF network which is a low computational rival for the Support vector machine (SVM) networks family. Results: The results showed suitable OSA detection percentage near 96% with a reduced complexity of nearly one third of the previously presented SVM based methods.


Classification, feature reduction, hybrid K-means recursive least-squares, multi-cluster feature selection, obstructive sleep apnea, single-lead electrocardiogram

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Hilmisson H, Lange N, Duntley SP. Sleep apnea detection: Accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index). Sleep Breath 2019;23:125-33.

Janbakhshi P, Shamsollahi MB. Sleep apnea detection from single-lead ECG using features based on ECG-derived respiration (EDR) signals. IRBM 2018;39:206-18.

Khandoker AH, Gubbi J, Palaniswami M. Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings. IEEE Trans Inf Technol Biomed 2009;13:1057-67.

Ma Y, Sun S, Zhang M, Guo D, Liu AR, Wei Y, et al. Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis. Sleep Breath 2019;24:231-40.

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;23:1011-21.

Hassan AR, Bashar SK, Bhuiyan MI. Computerized Obstructive

Sleep Apnea Diagnosis from Single-Lead ECG Signals Using Dual-Tree Complex Wavelet Transform. IEEE Region 10 Humanitarian Technology Conference (R10-HTC, Dhaka, Bangladesh); 21-23 December, 2017.

Nishad A, Pachori RB, Acharya UR. Application of TQWT based filterbank for sleep apnea screening using ECG signals. J Ambient Intell Humaniz Comput 2018. doi.org/10.1007/ s12652-018-0867-3.

Avc C, Akba A. Sleep apnea classification based on respiration

signals by using ensemble methods. Biomed Mater Eng 2015;26 Suppl 1:S1703-10.

Rachim VP, Li G, Chung WY. Sleep apnea classification using ECG-signal wavelet-PCA features. Biomed Mater Eng 2014;24:2875-82.

Cai D, Zhang C, He X. Unsupervised Feature Selection for Multi-Cluster Data. In Proceedings of 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2010. p. 333-42.

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;235:122-30.

Hassan AR, Haque MA. Computer-aided obstructive sleep apnea

screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating. Biocybern Biomed Eng 2016;36:256-66.

Hassan AR. Computer-aided obstructive sleep apnea detection

using normal inverse Gaussian parameters and adaptive boosting.

Biomed Signal Proc Control 2016;29:22-30.

Wang T, Lu C, Shen G, Hong F. Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network. PeerJ 2019;7:e7731.

Singh SA, Majumder S. A novel approach OSA detection using

single lead ECG Scalogram based on deep neural network. J Mech Med Biol 2019;19:1-18.

Urtnasan E, Park JU, Joo EY, Lee KJ. Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network. J Med Syst 2018;42:104.

Wang X, Cheng M, Wang Y, Liu S, Tian Z, Jiang F, et al. Obstructive Sleep Apnea Detection using ECG-Sensor with Convolutional Neural Networks. Multimedia Tools and Application, First Online; 18 June, 2018.

Wang T, Lu C, Shen G. Detection of sleep apnea from single-lead ECG signal using a time window artificial neural network. Hindawi Bio Med Res Int 2019.

Hassan AR, Haque MA, et al. Computer-Aided Sleep Apnea Diagnosis from Single-Lead Electrocardiogram using Dual-Tree Complex Wavelet Transform and spectral features. 1st International Conference on Electrical & Electronic Engineering (ICEEE), RUET, Rajshahi, Bangladesh; 04-06 November, 2015.

Thomas M, Das MK, Ari S. Automatic ECG arrhythmia classification using dual-tree complex wavelet based features. Int J Electron Commun 2015;69:715-21.

Rifkin RM. Everything Old is New Again: A Fresh Look atHistorical Approaches in Machine Learning. Ph.D. Thesis, MIT; 2002.

Haykin S. Neural Networks and Learning Machines. Pearson

Education, Inc., Upper Saddle River, New Jersey: Prentice Hall; 2008. p. 230-63.

Papini GB, Fonseca P, Margarito J, van Gilst MM, Overeem S, Bergmans JWM, et al. On the generalizability of ECG-based obstructive sleep apnea monitoring: Merits and limitations of the Apnea-ECG database. Conf Proc IEEE Eng Med Biol Soc 2018;2018:6022-5.

Zhang P, Peng J. SVM vs. Regularized Least Squares Classification. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK; August, 2004.

Debnath R, Takahashi H. Learning Capability: Classical RBF

Network vs. SVM with Gaussian Kernel. Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems; 2002. p. 293-302.


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