Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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.
Słowa kluczowe
Rocznik
Tom
Strony
5--12
Opis fizyczny
Bibliogr. 20 poz., schem., tab., wykr.
Twórcy
autor
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
autor
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
autor
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
autor
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
autor
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
autor
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
autor
- Centre for Computational Engineering & Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
Bibliografia
- [1] Altevogt, H. R. Colten and M. Bruce, Sleep Disorders and Sleep Deprivation, Washington DC, 2006.
- [2] 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.
- [3] 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.
- [4] 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.
- [5] 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.
- [6] 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.
- [7] 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.
- [8] 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.
- [9] 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.
- [10] 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.
- [11] 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.
- [12] 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.
- [13] 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.
- [14] 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.
- [15] 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.
- [16] N. Sadr and P. d. Chazal, "Automated Detection of Obstructive Sleep Apnoea by Single-lead ECG through ELM Classification," Computing in Cardiology, 2014.
- [17] 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.
- [18] "Physionet, CINC Challenge," [Online]. Available: www.physio net.com.
- [19] 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.
- [20] 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.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c8587325-2585-4d71-aec2-7816500decc6