Detection of Obstructive Sleep Apnea from ECG Signal using SVM based Grid Search
Abstract
Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening.References
Altevogt, H. R. Colten and M. Bruce, Sleep Disorders and Sleep Deprivation, Washington DC, 2006.
A. Jezzini, M. Ayache, L. Elkhansa and Z. a. a. Ibrahim, "ECG Classification for Sleep Apnea Detection," in IEEE, Lebanon, 2015, doi: 10.1109/ICABME.2015.7323312.
F. Mendonca, S. S. Mostafa, A. G. Ravelo-Garcia, F. Morgado-Dias and T. Penzel, "A Review of Obstructive Sleep Apnea Detection Approaches," IEEE, vol. 23, no. 2, 2018, doi:10.1109/JBHI.2018.2823 265.
L. Almazaydeh, K. Elleithy and M. Faezipour, "Obstructive Sleep Apnea Detection Using SVM-Based Classification of ECG Signal Features," in IEEE, California, 2012, doi:10.1109/EMBC.2012.63 47100.
P. de Chazal, C. Heneghan, E. Sheridan, R. Reilly, P. Nolan and M. O'Malley, "Automated Processing of the Single-Lead Electrocardiogram for the Detection of Obstructive Sleep Apnoea," IEEE, vol. 50, no. 6, 2003, doi: 10.1109/TBME.2003.812203.
A. R. Hassan, S. K. Bashar and M. I. H. Bhuiyan, "Computerized Obstructive Sleep Apnea Diagnosis from Single-lead ECG Signals using Dual-tree Complex Wavelet Transform," in IEEE, Dhaka, Bangladesh, 2017, doi: 10.1109/R10-HTC.2017.8288902.
W. C. Fang, I. W. Chen, S. H. Fan and C. K. Lee, "An Electrocardiography System Design for Obstructive Sleep Apnea Detection based on Improved Lomb Frequency Analysis Algorithm," in IEEE, Italy, 2017, doi: 10.1109/BIOCAS.2017.8325151.
A. Pinho, N. Pombo, B. M. Silva, K. Bousson and N. Garcia, "Towards an Accurate Sleep Apnea Detection Based on ECG Signal: The Quintessential of a Wise Feature Selection," Applied Soft Computing Journal, vol. 83, 2019, doi: 10.1016/j.asoc.2019.105568.
A. Thommandram, J. Eklund and C. McGrego, "Detection of Apnoea from Respiratory Time Series Data using Clinically Recognizable Features and kNN Classification," in IEEE, Osaka, Japan, 2013, doi: 10.1109/EMBC.2013.6610674.
A. Prabha, A. Trivedi, A. A. Kumar and C. S. Kumar, "Automated System for Obstructive Sleep Apnea Detection using Heart Rate Variability and Respiratory Rate Variability," in IEEE, Udupi, India, 2017, doi: 10.1109/ICACCI.2017.8126021.
M. Suchetha and A. Smruthy, "Real-Time Classification of Healthy and Apnea Subjects Using ECG Signals With Variational Mode Decomposition," IEEE Sensors Journal, vol. 17, p. 8, 2017, doi: 10.1109/JSEN.2017.2690805.
E. Goldshtein, A. Tarasiuk and Y. Zigel, "Automatic Detection of Obstructive Sleep Apnea Using Speech Signals," IEEE Transactions on Biomedical Engineering, vol. 58, p. 10, doi: 10.1109/TBME.2010.2 100096.
A. S. Ng, J. W. Chung, M. D. Gohel, W. W. Yu, K. L. Fan and T. K. Wong, "Evaluation of the Performance of Using Mean Absolute Amplitude Analysis of Thoracic and Abdominal Signals for Immediate Indication of Sleep Apnoea Events," Journal of Clinical Nursing, vol. 58, 2008, doi: 10.1111/j.1365-2702.2008.02323.x.
B. Xie and H. Minn, "Real Time Sleep Apnea Detection by Classifier Combination," IEEE Transactions on Information Technology in Biomedicine, 2012, doi: 10.1109/TITB.2012.2188299.
C. Varon, D. Testelmans, B. Buyse, J. Suykens and S. V. Huffel, "Sleep apnea classification using least-squares support vector machines on single lead ECG," in IEEE Engineering in Medicine and Biology Society (EMBC), 2013, doi: 10.1109/EMBC.2013.6610678.
N. Sadr and P. d. Chazal, "Automated Detection of Obstructive Sleep Apnoea by Single-lead ECG through ELM Classification," Computing in Cardiology, 2014.
L. Almazaydeh, K. Elleithy and M. Faezipour, "A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection," International Journal of Intelligent Systems and Applications in Engineering, p. 5, 2014, doi: 10.18201/ijisae.47487.
"Physionet, CINC Challenge," [Online]. Available: www.physio net.com.
P. Kathirvel, M. S. Manikandan, S. R. M. Prasanna and K. P. Soman, "An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator," Cardiovascular Engineering and Technology, vol. 2, p. 18, 2011, doi: 10.1007/s13239-011-0065-3.
R. G. John and K. I. Ramachandran, "Extraction of foetal ECG from abdominal ECG by nonlinear transformation and estimations," Computer Methods and Programs in Biomedicine, vol. 175, p. 12, 2019, doi: 10.1016/j.cmpb.2019.04.022.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 International Journal of Electronics and Telecommunications
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on https://creativecommons.org/licenses/by/4.0/.
2. Author’s Warranties
The author warrants that the article is original, written by stated author/s, has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author/s. The undersigned also warrants that the manuscript (or its essential substance) has not been published other than as an abstract or doctorate thesis and has not been submitted for consideration elsewhere, for print, electronic or digital publication.
3. User Rights
Under the Creative Commons Attribution license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions: 1. they must attribute the contribution in the manner specified by the author or licensor, 2. they may alter, transform, or build upon this work, 3. they may use this contribution for commercial purposes.
4. Rights of Authors
Authors retain the following rights:
- copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in own future works, including lectures and books,
- the right to reproduce the article for own purposes, provided the copies are not offered for sale,
- the right to self-archive the article
- the right to supervision over the integrity of the content of the work and its fair use.
5. Co-Authorship
If the article was prepared jointly with other authors, the signatory of this form warrants that he/she has been authorized by all co-authors to sign this agreement on their behalf, and agrees to inform his/her co-authors of the terms of this agreement.
6. Termination
This agreement can be terminated by the author or the Journal Owner upon two months’ notice where the other party has materially breached this agreement and failed to remedy such breach within a month of being given the terminating party’s notice requesting such breach to be remedied. No breach or violation of this agreement will cause this agreement or any license granted in it to terminate automatically or affect the definition of the Journal Owner. The author and the Journal Owner may agree to terminate this agreement at any time. This agreement or any license granted in it cannot be terminated otherwise than in accordance with this section 6. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
7. Royalties
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by the Journal Owner or its sublicensee.
8. Miscellaneous
The Journal Owner will publish the article (or have it published) in the Journal if the article’s editorial process is successfully completed and the Journal Owner or its sublicensee has become obligated to have the article published. Where such obligation depends on the payment of a fee, it shall not be deemed to exist until such time as that fee is paid. The Journal Owner may conform the article to a style of punctuation, spelling, capitalization and usage that it deems appropriate. The Journal Owner will be allowed to sublicense the rights that are licensed to it under this agreement. This agreement will be governed by the laws of Poland.
By signing this License, Author(s) warrant(s) that they have the full power to enter into this agreement. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
