Rhythm analysis of body surface potential mapping recordings from atrial fibrillation patients based on autocorrelation function_Journal of Biomedical Engineering

Authors：

ZHANG Qingzhou ¹ ,  YANG Cuiwei ^1,2,3 , BAI Baodan ⁴

1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China;
2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China;
3. Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, P.R.China;
4. School of Medical Instrument, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P.R.China;

Corresponding author：

YANG Cuiwei, Email: yangcw@fudan.edu.cn

Keywords：

atrial fibrillation; body surface potential mapping; autocorrelation function; fast Fourier transform; rhythm analysis

DOI：

10.7507/1001-5515.201706096

Video：

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The study of atrial fibrillation (AF) has been known as a hot topic of clinical concern. Body surface potential mapping (BSPM), a noninvasive electrical mapping technology, has been widely used in the study of AF. This study adopted 10 AF patients’ preoperative and postoperative BSPM data (each patient’s data contained 128 channels), and applied the autocorrelation function method to obtain the activation interval of the BSPM signals. The activation interval results were compared with that of manual counting method and the applicability of the autocorrelation function method was verified. Furthermore, we compared the autocorrelation function method with the commonly used fast Fourier transform (FFT) method. It was found that the autocorrelation function method was more accurate. Finally, to find a simple rule to predict the recurrence of atrial fibrillation, the autocorrelation function method was used to analyze the preoperative BSPM signals of 10 patients with persistent AF. Consequently, we found that if the patient’s proportion of channels with dominant frequency larger than 2.5 Hz in the anterior left region is greater than the other three regions (the anterior right region, the posterior left region, and the posterior right region), he or she might have a higher possibility of AF recurrence. This study verified the rationality of the autocorrelation function method for rhythm analysis and concluded a simple rule of AF recurrence prediction based on this method.

Citation： ZHANG Qingzhou, YANG Cuiwei, BAI Baodan. Rhythm analysis of body surface potential mapping recordings from atrial fibrillation patients based on autocorrelation function. Journal of Biomedical Engineering, 2018, 35(2): 161-170. doi: 10.7507/1001-5515.201706096 Copy

1.	Rieta J J, Castells F, Sánchez C, et al. Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Trans Biomed Eng, 2004, 51(7): 1176-1186.
2.	Kalsi M, Prakash N R. A new algorithm for detection of atrial fibrillation//2016 International conference on electrical, electronics, and optimization techniques (ICEEOT), 2016: 3177-3182.
3.	Ortigosa N, Cano O, Galbis A, et al. Time-Frequency analysis for early classification of persistent and Long-Standing persistent atrial fibrillation//2016 Computing In Cardiology Conference (CINC), 2016, 43: 673-676.
4.	Liu C, Eggen M D, Swingen C M, et al. Noninvasive mapping of transmural potentials during activation in swine hearts from body surface electrocardiograms. IEEE Trans Med Imaging, 2012, 31(9): 1777-1785.
5.	Zhou Zhaoye, Jin Qi, Chen Linyee, et al. Noninvasive imaging of High-Frequency drivers and Reconstruction of global dominant frequency Maps in patients with paroxysmal and persistent atrial fibrillation. IEEE Trans Biomed Eng, 2016, 63(6): 1333-1340.
6.	Han Chengzong, Liu Zhongming, Zhang Xin, et al. Noninvasive three-dimensional cardiac activation imaging from body surface potential maps: a computational and experimental study on a rabbit model. IEEE Trans Med Imaging, 2008, 27(11): 1622-1630.
7.	Guillem M S, Climent A M, Castells F, et al. Noninvasive mapping of human atrial fibrillation. J Cardiovasc Electrophysiol, 2009, 20(5): 507-513.
8.	Li G, He B. Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach. IEEE Trans Biomed Eng, 2001, 48(6): 660-669.
9.	He Bin, Li Guanglin, Zhang Xin. Noninvasive three-dimensional activation time imaging of ventricular excitation by means of a heart-excitation model. Phys Med Biol, 2002, 47(22): 4063-4078.
10.	He Bin, Li Guanglin, Zhang Xin. Noninvasive imaging of cardiac transmembrane potentials within three-dimensional myocardium by means of a realistic geometry anisotropic heart model. IEEE Trans Biomed Eng, 2003, 50(10): 1190-1202.
11.	Giffard-Roisin S, Jackson T, Fovargue L, et al. Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping. IEEE Trans Biomed Eng, 2017, 64(9): 2206-2218.
12.	Michal K, Herve R, Malgorzata F, et al. The effect of precordial lead displacement on p-wave morphology in body surface potential mapping. Comput Cardiol, 2013, 40: 531-534.
13.	王德玺, 杨翠微. 房颤病人体表标测信号的 f 波提取方法研究. 仪器仪表学报, 2016, 37(10): 2359-2365.
14.	Botteron G W, Smith J M. A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE Trans Biomed Eng, 1995, 42(6): 579-586.
15.	Li Wenhai, Yang Cuiwei, Wang Yanlei, et al. Several insights into the preprocessing of electrograms in atrial fibrillation for dominant frequency analysis. Biomed Eng Online, 2016, 15(1): 38.
16.	Ghanavati G, Hines P D, Lakoba T I. Understanding early indicators of critical transitions in power systems from autocorrelation functions. IEEE Transactions on Circuits and Systems I-Regular Papers, 2014, 61(9): 2747-2760.
17.	周拓. 基于心外膜标测技术的房颤电信号处理方法研究. 上海: 复旦大学, 2010.
18.	Ng J, Kadish A H, Goldberger J J. Technical considerations for dominant frequency analysis. J Cardiovasc Electrophysiol, 2007, 18(7): 757-764.
19.	Lemay M, Prudat Y, Jacquemet V, et al. Phase-rectified signal averaging used to estimate the dominant frequencies in ECG signals during atrial fibrillation. IEEE Trans Biomed Eng, 2008, 55(11): 2538-2547.

1. Rieta J J, Castells F, Sánchez C, et al. Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Trans Biomed Eng, 2004, 51(7): 1176-1186.
2. Kalsi M, Prakash N R. A new algorithm for detection of atrial fibrillation//2016 International conference on electrical, electronics, and optimization techniques (ICEEOT), 2016: 3177-3182.
3. Ortigosa N, Cano O, Galbis A, et al. Time-Frequency analysis for early classification of persistent and Long-Standing persistent atrial fibrillation//2016 Computing In Cardiology Conference (CINC), 2016, 43: 673-676.
4. Liu C, Eggen M D, Swingen C M, et al. Noninvasive mapping of transmural potentials during activation in swine hearts from body surface electrocardiograms. IEEE Trans Med Imaging, 2012, 31(9): 1777-1785.
5. Zhou Zhaoye, Jin Qi, Chen Linyee, et al. Noninvasive imaging of High-Frequency drivers and Reconstruction of global dominant frequency Maps in patients with paroxysmal and persistent atrial fibrillation. IEEE Trans Biomed Eng, 2016, 63(6): 1333-1340.
6. Han Chengzong, Liu Zhongming, Zhang Xin, et al. Noninvasive three-dimensional cardiac activation imaging from body surface potential maps: a computational and experimental study on a rabbit model. IEEE Trans Med Imaging, 2008, 27(11): 1622-1630.
7. Guillem M S, Climent A M, Castells F, et al. Noninvasive mapping of human atrial fibrillation. J Cardiovasc Electrophysiol, 2009, 20(5): 507-513.
8. Li G, He B. Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach. IEEE Trans Biomed Eng, 2001, 48(6): 660-669.
9. He Bin, Li Guanglin, Zhang Xin. Noninvasive three-dimensional activation time imaging of ventricular excitation by means of a heart-excitation model. Phys Med Biol, 2002, 47(22): 4063-4078.
10. He Bin, Li Guanglin, Zhang Xin. Noninvasive imaging of cardiac transmembrane potentials within three-dimensional myocardium by means of a realistic geometry anisotropic heart model. IEEE Trans Biomed Eng, 2003, 50(10): 1190-1202.
11. Giffard-Roisin S, Jackson T, Fovargue L, et al. Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping. IEEE Trans Biomed Eng, 2017, 64(9): 2206-2218.
12. Michal K, Herve R, Malgorzata F, et al. The effect of precordial lead displacement on p-wave morphology in body surface potential mapping. Comput Cardiol, 2013, 40: 531-534.
13. 王德玺, 杨翠微. 房颤病人体表标测信号的 f 波提取方法研究. 仪器仪表学报, 2016, 37(10): 2359-2365.
14. Botteron G W, Smith J M. A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE Trans Biomed Eng, 1995, 42(6): 579-586.
15. Li Wenhai, Yang Cuiwei, Wang Yanlei, et al. Several insights into the preprocessing of electrograms in atrial fibrillation for dominant frequency analysis. Biomed Eng Online, 2016, 15(1): 38.
16. Ghanavati G, Hines P D, Lakoba T I. Understanding early indicators of critical transitions in power systems from autocorrelation functions. IEEE Transactions on Circuits and Systems I-Regular Papers, 2014, 61(9): 2747-2760.
17. 周拓. 基于心外膜标测技术的房颤电信号处理方法研究. 上海: 复旦大学, 2010.
18. Ng J, Kadish A H, Goldberger J J. Technical considerations for dominant frequency analysis. J Cardiovasc Electrophysiol, 2007, 18(7): 757-764.
19. Lemay M, Prudat Y, Jacquemet V, et al. Phase-rectified signal averaging used to estimate the dominant frequencies in ECG signals during atrial fibrillation. IEEE Trans Biomed Eng, 2008, 55(11): 2538-2547.

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