Obstructive Sleep Apnea Diagnosis Using Mean Coat Clustering Algorithm and Wavelet Transform
محورهای موضوعی : Electrical Engineering
1 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
کلید واژه: obstructive sleep apneaT, symptom discount, hybrid recursive least squares okay-manner, multi cluster symptom choice,
چکیده مقاله :
The detection of obstructive sleep apnea (OSA) has turn out to be a warm studies topic because of the excessive danger of this sickness. in this paper, we tested a few effective and low-price computational sign processing techniques for this undertaking and compared their effects with current achievements in OSA detection. dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. 8 nonlinear features are extracted from those coefficients after which decreased the usage of a multi-cluster characteristic selection (MCFS) algorithm. The remaining functions are implemented to a hybrid "ok-approach, RLS" RBF network, which is a small computational rival for the support vector device (SVM) own family of networks. The results confirmed a appropriate OSA detection percentage near 96% with a discounted complexity of virtually one-third of previously provided SVM-primarily based techniques.
The detection of obstructive sleep apnea (OSA) has turn out to be a warm studies topic because of the excessive danger of this sickness. in this paper, we tested a few effective and low-price computational sign processing techniques for this undertaking and compared their effects with current achievements in OSA detection. dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. 8 nonlinear features are extracted from those coefficients after which decreased the usage of a multi-cluster characteristic selection (MCFS) algorithm. The remaining functions are implemented to a hybrid "ok-approach, RLS" RBF network, which is a small computational rival for the support vector device (SVM) own family of networks. The results confirmed a appropriate OSA detection percentage near 96% with a discounted complexity of virtually one-third of previously provided SVM-primarily based techniques.
1. 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.
2. 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.
3. Khandoker AH, Gubbi J, Palaniswami M. Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings. IEEE Trans InfTechnol Biomed. 2009;13:1057–67. 4. 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.
5. 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.
6. Hassan AR, Bashar SK, Bhuiyan MI. Computerized Obstructive Sleep Apnea Diagnosis from Single-Lead ECG Signals Using Dual-Tree Complex Wavelet Transform. IEEE Region Humanitarian Technology Conference (R-HTC, Dhaka, Bangladesh); 21-23 December. 2017
7. Nishad A, Pachori RB, Acharya UR. Application of TQWT based filterbank for sleep apnea screening using ECG signals. J Ambient IntellHumanizComput. 2018 doiorg/101007/s12652-018-0867-3.
8. Avcı C, Akbaş A. Sleep apnea classification based on respiration signals by using ensemble methods. Biomed Mater Eng. 2015;26(Suppl 1):S1703–10.
9. Rachim VP, Li G, Chung WY. Sleep apnea classification using ECG-signal wavelet-PCA features. Biomed Mater Eng. 2014;24:2875–82.
10. 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. 2010:333–42.
11. 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.
12. 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.
13. Hassan AR. Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting. Biomed Signal Proc Control. 2016;29:22–30.
14. 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.
15. 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.
16. 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.
17. 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. 2018 Jun 18;
18. 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 Article ID 9768072. https://doiorg/101155/2019/9768072.
19. Hassan AR, Haque MA, et al. Computer-Aided Sleep Apnea Diagnosis from Single-Lead Electrocardiogram using Dual-Tree Complex Wavelet Transform and spectral features. Rajshahi, Bangladesh: 1st International Conference on Electrical & Electronic Engineering (ICEEE), RUET; 2015. Nov, 04-06.
20. 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.
21. Rifkin RM. Everything Old is New Again: A Fresh Look at Historical Approaches in Machine Learning. Ph.D. Thesis, MIT. 2002
22. Haykin S. Neural Networks and Learning Machines.Pearson Education, Inc. Upper Saddle River, New Jersey: Prentice Hall; 2008. pp. 230–63.
23. 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. ConfProc IEEE Eng Med Biol Soc. 2018;2018:6022–5.
24. Zhang P, Peng J. SVM vs. Regularized Least Squares Classification Proceedings of the th International Conference on Pattern Recognition. Cambridge, UK: 2004. Aug,
25. 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. 2002:293–302.
