Detection algorithm of paroxysmal atrial fibrillation with sparse coding based on Riemannian manifold_Journal of Biomedical Engineering

Authors：

MENG Xianhui ,  LIU Ming , XIONG Peng , CHEN Jian , YANG Lin , LIU Xiuling

Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;

Corresponding author：

LIU Ming, Email: gleer@126.com

Keywords：

atrial fibrillation; electrocardiogram; covariance matrix; Riemannian manifolds; sparse coding

DOI：

10.7507/1001-5515.201907001

Video：

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In order to solve the problem that the early onset of paroxysmal atrial fibrillation is very short and difficult to detect, a detection algorithm based on sparse coding of Riemannian manifolds is proposed. The proposed method takes into account that the nonlinear manifold geometry is closer to the real feature space structure, and the computational covariance matrix is used to characterize the heart rate variability (RR interval variation), so that the data is in the Riemannian manifold space. Sparse coding is applied to the manifold, and each covariance matrix is represented as a sparse linear combination of Riemann dictionary atoms. The sparse reconstruction loss is defined by the affine invariant Riemannian metric, and the Riemann dictionary is learned by iterative method. Compared with the existing methods, this method used shorter heart rate variability signal, the calculation was simple and had no dependence on the parameters, and the better prediction accuracy was obtained. The final classification on MIT-BIH AF database resulted in a sensitivity of 99.34%, a specificity of 95.41% and an accuracy of 97.45%. At the same time, a specificity of 95.18% was realized in MIT-BIH NSR database. The high precision paroxysmal atrial fibrillation detection algorithm proposed in this paper has a potential application prospect in the long-term monitoring of wearable devices.

Citation： MENG Xianhui, LIU Ming, XIONG Peng, CHEN Jian, YANG Lin, LIU Xiuling. Detection algorithm of paroxysmal atrial fibrillation with sparse coding based on Riemannian manifold. Journal of Biomedical Engineering, 2020, 37(4): 683-691. doi: 10.7507/1001-5515.201907001 Copy

1.	Young M. Atrial fibrillation. Crit Care Nurs Clin N Am, 2019, 31(1): 77-90.
2.	Markides V. Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment. Heart, 2003, 89(8): 939-943.
3.	Camm A J, Kirchhof P, Lip G Y H, <italic>et al</italic>. Guidelines for the management of atrial fibrillation. Eur Heart J, 2010, 12(19): 2369-2429.
4.	García M, Ródenas J, Alcaraz R, <italic>et al</italic>. Application of the relative wavelet energy to heart rate Independent detection of atrial fibrillation. Comput Methods Programs Biomed, 2016, 131: 157-168.
5.	Du Xiaochuan, Rao N, Qian Mengyao, <italic>et al</italic>. A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann Noninvasive Electrocardiol, 2014, 19(3): 217-225.
6.	Ródenas J, García M, Alcaraz R, <italic>et al</italic>. Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy, 2015, 17(12): 6179-6199.
7.	Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Methods Programs Biomed, 2012, 105(1): 40-49.
8.	Tateno K, Glass L. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals. Med Biol Eng Comput, 2001, 39(6): 664-671.
9.	Li Yanjun, Tang Xiaoying, Wang Ancong, <italic>et al</italic>. Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation. Australas Phys Eng Sci Med, 2017, 40(3): 707-716.
10.	Kennedy A, Finlay D D, Guldenring D, <italic>et al</italic>. Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification. J Electrocardiol, 2016, 49(6): 871-876.
11.	Petrėnas A, Sörnmo L, Lukoševičius A, <italic>et al</italic>. Detection of occult paroxysmal atrial fibrillation. Med Biol Eng Comput, 2015, 53(4): 287-297.
12.	Shashikumar S P, Shah A J, Clifford G D, et al. Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London: SIGMOD, SIGKDD, 2018: 715-723.
13.	Cui X, Chang E, Yang W H, <italic>et al</italic>. Automated detection of paroxysmal atrial fibrillation using an information-based similarity approach. Entropy, 2017, 19(12): 677.
14.	Devanne M, Wannous H, Berretti S, <italic>et al</italic>. 3-D human action recognition by shape analysis of motion trajectories on Riemannian manifold. IEEE Trans Cybern, 2015, 45(7): 1340-1352.
15.	de Souza M. On a class of nonhomogeneous elliptic equation on compact Riemannian manifold without boundary. Mediterranean Journal of Mathematics, 2018, 15(3): 101.
16.	Fletcher P T, Joshi S. Riemannian geometry for the statistical analysis of diffusion tensor data. Signal Processing, 2007, 87(2): 250-262.
17.	Pennec X, Fillard P, Ayache N. A Riemannian framework for tensor computing. Int J Comput Vis, 2006, 66(1): 41-66.
18.	Cherian A, Sra S. Riemannian dictionary learning and sparse coding for positive definite matrices. IEEE Trans Neural Netw Learn Syst, 2017, 28(12): 2859-2871.
19.	Harandi M T, Hartley R, Lovell B, <italic>et al</italic>. Sparse coding on symmetric positive definite manifolds using Bregman divergences. IEEE Trans Neural Netw Learn Syst, 2016, 27(6): 1294-1306.
20.	Absil P A, Mahony R, Sepulchre R. Optimization algorithms on matrix manifold. New Jersey: Princeton University Press, 2008: 54-90.
21.	Goldberger A L, Amaral L N, Glass L, <italic>et al</italic>. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): e215-e220.
22.	Andersen R S, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation. Expert Syst Appl, 2019, 115: 465-473.
23.	Sepulveda-Suescun J P, Murillo-Escobar J, Urda-Benitez R D, <italic>et al</italic>. Atrial fibrillation detection through heart rate variability using a machine learning approach and Poincare plot features//VⅡ Latin American Congress on Biomedical Engineering CLAIB 2016. Bucaramanga: IFMBE, 2016: 565-568.
24.	Zhou Xiaolin, Ding Hongxia, Ung B, <italic>et al</italic>. Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy. Biomed Eng Online, 2014, 13(1): 18.
25.	Lian Jie, Wang Lian, Muessig D. A simple method to detect atrial fibrillation using RR intervals. Am J Cardiol, 2011, 107(10): 1494-1497.

1. Young M. Atrial fibrillation. Crit Care Nurs Clin N Am, 2019, 31(1): 77-90.
2. Markides V. Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment. Heart, 2003, 89(8): 939-943.
3. Camm A J, Kirchhof P, Lip G Y H, <italic>et al</italic>. Guidelines for the management of atrial fibrillation. Eur Heart J, 2010, 12(19): 2369-2429.
4. García M, Ródenas J, Alcaraz R, <italic>et al</italic>. Application of the relative wavelet energy to heart rate Independent detection of atrial fibrillation. Comput Methods Programs Biomed, 2016, 131: 157-168.
5. Du Xiaochuan, Rao N, Qian Mengyao, <italic>et al</italic>. A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann Noninvasive Electrocardiol, 2014, 19(3): 217-225.
6. Ródenas J, García M, Alcaraz R, <italic>et al</italic>. Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy, 2015, 17(12): 6179-6199.
7. Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Methods Programs Biomed, 2012, 105(1): 40-49.
8. Tateno K, Glass L. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals. Med Biol Eng Comput, 2001, 39(6): 664-671.
9. Li Yanjun, Tang Xiaoying, Wang Ancong, <italic>et al</italic>. Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation. Australas Phys Eng Sci Med, 2017, 40(3): 707-716.
10. Kennedy A, Finlay D D, Guldenring D, <italic>et al</italic>. Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification. J Electrocardiol, 2016, 49(6): 871-876.
11. Petrėnas A, Sörnmo L, Lukoševičius A, <italic>et al</italic>. Detection of occult paroxysmal atrial fibrillation. Med Biol Eng Comput, 2015, 53(4): 287-297.
12. Shashikumar S P, Shah A J, Clifford G D, et al. Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London: SIGMOD, SIGKDD, 2018: 715-723.
13. Cui X, Chang E, Yang W H, <italic>et al</italic>. Automated detection of paroxysmal atrial fibrillation using an information-based similarity approach. Entropy, 2017, 19(12): 677.
14. Devanne M, Wannous H, Berretti S, <italic>et al</italic>. 3-D human action recognition by shape analysis of motion trajectories on Riemannian manifold. IEEE Trans Cybern, 2015, 45(7): 1340-1352.
15. de Souza M. On a class of nonhomogeneous elliptic equation on compact Riemannian manifold without boundary. Mediterranean Journal of Mathematics, 2018, 15(3): 101.
16. Fletcher P T, Joshi S. Riemannian geometry for the statistical analysis of diffusion tensor data. Signal Processing, 2007, 87(2): 250-262.
17. Pennec X, Fillard P, Ayache N. A Riemannian framework for tensor computing. Int J Comput Vis, 2006, 66(1): 41-66.
18. Cherian A, Sra S. Riemannian dictionary learning and sparse coding for positive definite matrices. IEEE Trans Neural Netw Learn Syst, 2017, 28(12): 2859-2871.
19. Harandi M T, Hartley R, Lovell B, <italic>et al</italic>. Sparse coding on symmetric positive definite manifolds using Bregman divergences. IEEE Trans Neural Netw Learn Syst, 2016, 27(6): 1294-1306.
20. Absil P A, Mahony R, Sepulchre R. Optimization algorithms on matrix manifold. New Jersey: Princeton University Press, 2008: 54-90.
21. Goldberger A L, Amaral L N, Glass L, <italic>et al</italic>. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): e215-e220.
22. Andersen R S, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation. Expert Syst Appl, 2019, 115: 465-473.
23. Sepulveda-Suescun J P, Murillo-Escobar J, Urda-Benitez R D, <italic>et al</italic>. Atrial fibrillation detection through heart rate variability using a machine learning approach and Poincare plot features//VⅡ Latin American Congress on Biomedical Engineering CLAIB 2016. Bucaramanga: IFMBE, 2016: 565-568.
24. Zhou Xiaolin, Ding Hongxia, Ung B, <italic>et al</italic>. Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy. Biomed Eng Online, 2014, 13(1): 18.
25. Lian Jie, Wang Lian, Muessig D. A simple method to detect atrial fibrillation using RR intervals. Am J Cardiol, 2011, 107(10): 1494-1497.

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