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Volume 11, Issue 2 (Spring 2023) |
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Identification of Atrial Arrhythmia Foci Using ECG Signal and the Effect of Autonomic Nervous System
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Fatemeh Mohammadi , Ali Sheikhani * , Farbod Razzazi , Alireza Ghorbani Sharif |
Department of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran , sheikhani_al_81@srbiau.ac.ir |
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Abstract: (1334 Views) |
Introduction: Any disturbance in the rhythm of the heartbeat is called cardiac arrhythmia. One of the most common methods of treating cardiac arrhythmias is radiofrequency ablation. Determining the position of the heart to be ablated is performed by a specialist physician during electrophysiological studies of the heart (EPS) and under X-ray radiation. ECG signal is a non-invasive, safe, and fast tool for understanding the electrical activity of the heart. On the other hand, the cardiac nervous system also controls the electrical activity of the heart. The present study aimed to identify focal atrial arrhythmia using ECG signal properties and to evaluate the effect of the autonomic nervous system in the treatment of cardiac arrhythmias. Materials and Methods: The standard 12-lead ECG signal was recorded in patients with atrial arrhythmia. The pulses of each signal are isolated by using the Pan-Tompkins algorithm and the properties of each pulse are extracted by the independent component analysis. Based on the extracted features and dictionary of the sparse decomposition algorithm, the locations of the arrhythmia foci inside the left atrium, right atrium, and septum were estimated. The function of the autonomic nervous system was also examined using the frequency characteristics of the heart rate variability signal. Results: The classification of the characteristics of each ECG signal from 52 patients determines the location of the arrhythmia foci within the right and left atrium and septum. Findings show that ECG signal analysis can estimate the location of ablation in the left or right atrium with more than 93% accuracy. Examination of sympathetic and parasympathetic balance in the autonomic nervous system using frequency analysis of HRV signal shows that this system plays an important role in exacerbating or improving cardiac arrhythmias. Conclusion: ECG signal analysis of patients with atrial tachycardia is a suitable and accurate tool to identify the location of the arrhythmia foci before EPS and ablation. Maintaining the balance of the cardiovascular system is effective in treating arrhythmias and further clinical studies in this field will help increase the effectiveness of cardiac arrhythmia treatment.
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Keywords: Electrocardiography, Electrophysiology, Arrhythmias, Cardiac, Autonomic Nervous System |
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Full-Text [PDF 1005 kb]
(850 Downloads)
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Type of Study: Research --- Open Access, CC-BY-NC |
Subject:
Basic research in Neuroscience
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References |
1. Kistler P M, Roberts-Thomson K C, Haqqani H M, Fynn S P, Singarayar S, et al. P-wave morphology in focal atrial tachycardia: development of an algorithm to predict the anatomic site of origin. Journal of the American College of Cardiology, 2006; 48(5): 1010-017. [ DOI:10.1016/j.jacc.2006.03.058] 2. Teh A W, Kistler P M, & Kalman J M, Using the 12‐Lead ECG to Localize the Origin of Ventricular and Atrial Tachycardias: Part 1. Focal Atrial Tachycardia: CME. Journal of cardiovascular electrophysiology, 2009; 20(6): 706-09. [ DOI:10.1111/j.1540-8167.2009.01456.x] 3. Shah A J, Lim, H S, Yamashita, S, Zellerhoff, S, Berte, B, Mahida, et al. Non invasive ecg mapping to guide catheter ablation. Journal of atrial fibrillation, 2014; 7(3). 4. Alday E A P, Colman M A, Langley P, Butters T D, Higham, et al. A New Algorithm to Diagnose Atrial Ectopic Origin from Multi Lead ECG Systems - Insights from 3D Virtual Human Atria and Torso, PLOS Comput. Biol., 2015; 11(1): 1-15. [ DOI:10.1371/journal.pcbi.1004026] 5. Provost J, Costet A, Wan E, Gambhir A, Whang W, Garan H, & Konofagou E E, Assessing the atrial electromechanical coupling during atrial focal tachycardia, flutter, and fibrillation using electromechanical wave imaging in humans, Computers in biology and medicine, 2015; 65: 161-67. [ DOI:10.1016/j.compbiomed.2015.08.005] 6. Ramanathan C, Ghanem R N, Jia P, Ryu K, & Rudy Y, Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nature medicine, 2004; 10(4): 422-28. [ DOI:10.1038/nm1011] 7. Falahati M, Abbaszadeh M & Zokaei M, Common Methods in the Analysis of Heart Rate Variability: A Review Study, Iran Occupational Health, 202; 17(43): 1-13. 8. Baselli G, Cerutti S, Civardi S, Lombardi F, Malliani A., Merri M, ... & Rizzo G, Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. International journal of bio-medical computing, 1987; 20(1-2): 51-70. [ DOI:10.1016/0020-7101(87)90014-6] 9. Casolo G C, Stroder P, Signorini C, Calzolari F, Zucchini M, Balli E, ... & Lazzerini S, Heart rate variability during the acute phase of myocardial infarction. Circulation, 1992; 85(6): 2073-2079. [ DOI:10.1161/01.CIR.85.6.2073] 10. Fioranelli M, Piccoli M, Mileto G M, Sgreccia F, Azzolini, et al. Analysis of heart rate variability five minutes before the onset of paroxysmal atrial fibrillation. Pacing and clinical electrophysiology, 1999; 22(5): 743-749. [ DOI:10.1111/j.1540-8159.1999.tb00538.x] 11. Lombardi F, Colombo A, Basilico B, Ravaglia R, Garbin, M., Vergani,et al. Heart rate variability and early recurrence of atrial fibrillation after electrical cardioversion. Journal of the American College of Cardiology, 2001; 37(1): 157-62. [ DOI:10.1016/S0735-1097(00)01039-1] 12. Khan A A, Lip G Y, & Shantsila A, Heart rate variability in atrial fibrillation: The balance between sympathetic and parasympathetic nervous system. European journal of clinical investigation, 2019; 49(11). [ DOI:10.1111/eci.13174] 13. Pan J & Tompkins W J, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng, 1985; 32(3): 230-36. [ DOI:10.1109/TBME.1985.325532] 14. Uhm J S, Shim J, Wi J, Mun H S, Pak H N, et al. An electrocardiography algorithm combined with clinical features could localize the origins of focal atrial tachycardias in adjacent structures. Europace, 2014; 16(7): 1061-068. [ DOI:10.1093/europace/eut393] 15. MS Lee J, & P Fynn S, P wave morphology in guiding the ablation strategy of focal atrial tachycardias and atrial flutter. Current cardiology reviews, 2015; 11(2): 103-110. [ DOI:10.2174/1573403X10666141013121252] 16. Ghandeharion H & Erfanian Omidvar A, Common Methods in the Analysis of Heart Rate Variability: A Review Study, Iranian Jiurnal of Biomedical Engineering, 2009; 3(3): 199-212. 17. Jiang X, Zhang L, Zhao Q, & Albayrak S, ECG arrhythmias recognition system based on independent component analysis feature extraction. In TENCON 2006-2006 IEEE Region 10 Conference IEEE, 2006; 10: 1-4. [ DOI:10.1109/TENCON.2006.343781] 18. Dash M, & Liu H, Feature selection for classification. Intelligent data analysis, 1997; 1(3): 131-56. [ DOI:10.3233/IDA-1997-1302] 19. Wu Y, & Zhang L, ECG classification using ICA features and support vector machines. In International Conference on Neural Information Processing, Springer, Berlin, Heidelberg, 2011; 146-54. [ DOI:10.1007/978-3-642-24955-6_18] 20. Hyvarinen A, Fast and robust fixed-point algorithms for independent component analysis. IEEE transactions on Neural Networks, 1999; 10(3): 626-34. [ DOI:10.1109/72.761722] 21. Mallat S G & Zhang Z, Matching pursuits with time-frequency dictionaries. IEEE Transactions on signal processing, 1993; 41(12): 3397-415. [ DOI:10.1109/78.258082] 22. Drémeau A, Herzet C, & Daudet L, Boltzmann machine and mean-field approximation for structured sparse decompositions. IEEE Transactions on Signal Processing, 2012; 60(7): 3425-438. [ DOI:10.1109/TSP.2012.2192436] 23. Yang J, Peng Y, Xu W & Dai Q, Ways to sparse representation: an overview. Science in China series F: information sciences, 2009; 52(4): 695-703. [ DOI:10.1007/s11432-009-0045-5]
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Mohammadi F, Sheikhani A, Razzazi F, Ghorbani Sharif A. Identification of Atrial Arrhythmia Foci Using ECG Signal and the Effect of Autonomic Nervous System. Shefaye Khatam 2023; 11 (2) :65-74 URL: http://shefayekhatam.ir/article-1-2308-en.html
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