Automatic detection and classification of atrial fibrillation using RR intervals and multi-eigenvalue_Journal of Biomedical Engineering

Authors：

CHEN Zhibo , LI Jian ,  LI Zhi , PENG Yuntao , GAO Xingjiao

School of Electronic Information, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

LI Zhi, Email: lizhi@scu.edu.cn

Keywords：

atrial fibrillation; robust coefficient of variation; skewness; Lempel-Ziv complexity; support vector machine

DOI：

10.7507/1001-5515.201710050

Video：

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Abstract Full text Figures/Tables Video References Cited by

Atrial fibrillation (AF) is a common arrhythmia disease. Detection of atrial fibrillation based on electrocardiogram (ECG) is of great significance for clinical diagnosis. Due to the non-linearity and complexity of ECG signals, the procedure to manually diagnose the ECG signals takes a lot of time and is prone to errors. In order to overcome the above problems, a feature extraction method based on RR interval is proposed in this paper. The discrete degree of RR interval is described with the robust coefficient of variation (RCV), the distribution shape of RR interval is described with the skewness parameter (SKP), and the complexity of RR interval is described with the Lempel-Ziv complexity (LZC). Finally, the feature vectors of RCV, SKP, and LZC are input into the support vector machine (SVM) classifier model to achieve automatic classification and detection of atrial fibrillation. To verify the validity and practicability of the proposed method, the MIT-BIH atrial fibrillation database was used to verify the data. The final classification results show that the sensitivity is 95.81%, the specificity is 96.48%, the accuracy is 96.09%, and the specificity of 95.16% is achieved in the MIT-BIH normal sinus rhythm database. The experimental results show that the proposed method is an effective classification method for atrial fibrillation.

Citation： CHEN Zhibo, LI Jian, LI Zhi, PENG Yuntao, GAO Xingjiao. Automatic detection and classification of atrial fibrillation using RR intervals and multi-eigenvalue. Journal of Biomedical Engineering, 2018, 35(4): 550-556. doi: 10.7507/1001-5515.201710050 Copy

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2.	Healey J S, Connolly S J, Gold M R, et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med, 2012, 366(2): 120-129.
3.	Sanna T, Diener H C, Passman R S, et al. Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med, 2014, 370(26): 2478-2486.
4.	Barrett P M, Komatireddy R, Haaser S, et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med, 2014, 127(1): 95.e11-95.e17.
5.	Lowres N, Neubeck L, Salkeld G, et al. Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost, 2014, 111(6): 1167-1176.
6.	Harris K, Edwards D, Mant J. How can we best detect atrial fibrillation? J R Coll Physicians Edinb, 2012, 42(Suppl 18): 5-22.
7.	魏晓玲, 刘明, 苑新, 等. 基于多特征融合与卷积神经网络的房颤检测. 激光杂志, 2017, 38(5): 176-179.
8.	Ladavich S, Ghoraani B. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomed Signal Process Control, 2015, 18: 274-281.
9.	Weng Binwei, Wang J J, Michaud F, et al. Atrial fibrillation detection using stationary wavelet transform analysis. Conf Proc IEEE Eng Med Biol Soc, 2008, (2008): 1128-1131.
10.	Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med, 2015, 60: 132-142.
11.	Babaeizadeh S, Gregg R E, Helfenbein E D, et al. Improvements in atrial fibrillation detection for real-time monitoring. J Electrocardiol, 2009, 42(6): 522-526.
12.	Parvaresh S, Ayatollahi A. Automatic atrial fibrillation detection using autoregressive modeling//The International Conference on Biomedical Engineering and Technology. Kuala Lumpur: Asia-Pacific Chemical, Biological & Environmental Engineering Society, 2011: 105-108.
13.	武扬. 心电特征提取及分类方法研究. 上海: 上海交通大学, 2012.
14.	Yazdani S, Vesin J M. Adaptive mathematical morphology for QRS fiducial points detection in the ECG//The 41st Annual International Conference of Computing in Cardiology, Cambridge, MA: MIT's Laboratory for Computational Physiology, 2014: 725-728.
15.	Yeh Y C, Wang Wenjune. QRS complexes detection for ECG signal: the difference operation method. Comput Methods Programs Biomed, 2008, 91(3): 245-254.
16.	Li Yanjun, Tang Xiaoying, Wang Ancong, et al. Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation. Australasian Physical & Engineering Sciences in Medicine, 2017, 40(3): 707-716.
17.	赵勇, 洪文学, 孙士博. 基于多特征和支持向量机的心律失常分类. 生物医学工程学杂志, 2011, 28(2): 292-295.
18.	Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
19.	Lempel A, Ziv J. On the complexity of finite sequences. IEEE Transactions on Information Theory, 1976, 22(1): 75-81.
20.	Byun H, Lee S W. A survey on pattern recognition applications of support vector machines. International Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(3): 459-486.
21.	Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3, SI): 389-396.
22.	Du Xiaochuan, Rao N, Qian Mengyao, et al. A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann Noninvasive Electrocardiol, 2014, 19(3): 217-225.
23.	García M, Ródenas J, Alcaraz R, et al. Application of the relative wavelet energy to heart rate Independent detection of atrial fibrillation. Comput Methods Programs Biomed, 2016, 131: 157-168.
24.	Kennedy A, Finlay D D, Guldenring D, et al. Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification. J Electrocardiol, 2016, 49(6): 871-876.
25.	Afdala A, Nuryani N, Nugroho A S. Automatic detection of atrial fibrillation using basic shannon entropy of RR interval feature. International Conference on Science and Applied Science. Indonesia: IOPScience, 2017, 795(1): 012038.

1. Chugh S S, Havmoeller R, Narayanan K, et al. Worldwide epidemiology of atrial fibrillation a global burden of disease 2010 study. Circulation, 2014, 129(8): 837-847.
2. Healey J S, Connolly S J, Gold M R, et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med, 2012, 366(2): 120-129.
3. Sanna T, Diener H C, Passman R S, et al. Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med, 2014, 370(26): 2478-2486.
4. Barrett P M, Komatireddy R, Haaser S, et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med, 2014, 127(1): 95.e11-95.e17.
5. Lowres N, Neubeck L, Salkeld G, et al. Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost, 2014, 111(6): 1167-1176.
6. Harris K, Edwards D, Mant J. How can we best detect atrial fibrillation? J R Coll Physicians Edinb, 2012, 42(Suppl 18): 5-22.
7. 魏晓玲, 刘明, 苑新, 等. 基于多特征融合与卷积神经网络的房颤检测. 激光杂志, 2017, 38(5): 176-179.
8. Ladavich S, Ghoraani B. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomed Signal Process Control, 2015, 18: 274-281.
9. Weng Binwei, Wang J J, Michaud F, et al. Atrial fibrillation detection using stationary wavelet transform analysis. Conf Proc IEEE Eng Med Biol Soc, 2008, (2008): 1128-1131.
10. Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med, 2015, 60: 132-142.
11. Babaeizadeh S, Gregg R E, Helfenbein E D, et al. Improvements in atrial fibrillation detection for real-time monitoring. J Electrocardiol, 2009, 42(6): 522-526.
12. Parvaresh S, Ayatollahi A. Automatic atrial fibrillation detection using autoregressive modeling//The International Conference on Biomedical Engineering and Technology. Kuala Lumpur: Asia-Pacific Chemical, Biological & Environmental Engineering Society, 2011: 105-108.
13. 武扬. 心电特征提取及分类方法研究. 上海: 上海交通大学, 2012.
14. Yazdani S, Vesin J M. Adaptive mathematical morphology for QRS fiducial points detection in the ECG//The 41st Annual International Conference of Computing in Cardiology, Cambridge, MA: MIT's Laboratory for Computational Physiology, 2014: 725-728.
15. Yeh Y C, Wang Wenjune. QRS complexes detection for ECG signal: the difference operation method. Comput Methods Programs Biomed, 2008, 91(3): 245-254.
16. Li Yanjun, Tang Xiaoying, Wang Ancong, et al. Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation. Australasian Physical & Engineering Sciences in Medicine, 2017, 40(3): 707-716.
17. 赵勇, 洪文学, 孙士博. 基于多特征和支持向量机的心律失常分类. 生物医学工程学杂志, 2011, 28(2): 292-295.
18. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
19. Lempel A, Ziv J. On the complexity of finite sequences. IEEE Transactions on Information Theory, 1976, 22(1): 75-81.
20. Byun H, Lee S W. A survey on pattern recognition applications of support vector machines. International Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(3): 459-486.
21. Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3, SI): 389-396.
22. Du Xiaochuan, Rao N, Qian Mengyao, et al. A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann Noninvasive Electrocardiol, 2014, 19(3): 217-225.
23. García M, Ródenas J, Alcaraz R, et al. Application of the relative wavelet energy to heart rate Independent detection of atrial fibrillation. Comput Methods Programs Biomed, 2016, 131: 157-168.
24. Kennedy A, Finlay D D, Guldenring D, et al. Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification. J Electrocardiol, 2016, 49(6): 871-876.
25. Afdala A, Nuryani N, Nugroho A S. Automatic detection of atrial fibrillation using basic shannon entropy of RR interval feature. International Conference on Science and Applied Science. Indonesia: IOPScience, 2017, 795(1): 012038.

Journal of Biomedical Engineering

Automatic detection and classification of atrial fibrillation using RR intervals and multi-eigenvalue

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