بهره‌گیری از نقشه‌ی بازگشتی متقاطع لیدهای سینه‌ای، برای تشخیص محل مسیرهای فرعی در بیماران دارای نشانگان ولف-پارکینسون-وایت
محورهای موضوعی : مهندسی پزشکی
سکینه یحیی زاده ساروی
1
,
نادر جعفرنیا دابانلو
2
,
علی مطیع نصرآبادی
3
,
علیرضا قربانی شریف
4
1 - گروه مهندسی پزشکی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - گروه مهندسی پزشکی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
3 - گروه مهندسی پزشکی، دانشگاه شاهد، تهران، ایران
4 - کلینیک آریتمی دکتر قربانی شریف، تهران، ایران
کلید واژه: تعیین محل, راه فرعی, سندروم ولف-پارکینسون-وایت, نقشه‌ی بازگشتی متقاطع,
چکیده مقاله :
تعیین محل غیرتهاجمی راه فرعی (AP) در بیماران با سندروم ولف-پارکینسون-وایت (WPW) اغلب با تشخیص پزشک از طریق مشاهده سیگنال الکتروکاردیوگرام(ECG) انجام می‌شود و گاهی تشخیص پزشکان در این خصوص با هم متفاوت است. بنابراین یک روش خودکار با صحت بالا می‌تواند این تفاوت را کاهش دهد. هدف این مطالعه، تعیین محل نیمه‌خودکار AP در بیماران WPW با استفاده از ویژگی‌های استخراج شده از نقشه‌ی بازگشتی متقاطع (CRP) لیدهای سینه‌ای متوالی ECG است. شرکت‌کنندگان شامل 31 بیمار WPWی آشکار (69-8 سال، با میانگین سنی 31.19±14.69سال، 32.3%خانم) هستند که در اولین جلسه، از طریق ابلیشن درمان شدند. ویژگی‌های استخراج شده از CRP لیدهای سینه‌ای متوالی، شامل لامیناریتی(LAM) ، ترپ‌تایم (TT)، دترمینیسم (DET) و میانگین طول خطوط قطری(L) محاسبه شدند. برای کاهش ویژگی، روش جستجوی مستقیم ترتیبی (SFS)، برای طبقه‌بندی روش K– نزدیک‌ترین همسایه(KNN) و برای اعتبارسنجی متقابل (CV) روش LOO استفاده شد. روش ارائه شده توانست APهای راست و چپ را در بیماران WPW با صحت 87% (حساسیت: 93.33%، اختصاصیت: 81.25%) تمایز دهد. این نتیجه با استفاده از ویژگی‌های LAM و L استخراج شده به ترتیب از CRP لیدهای V1 و V2 و CRP لیدهای V3 و V4 به دست آمد.
The non-invasive localization of accessory pathway (AP) in patients with Wolff-Parkinson-White (WPW) syndrome is typically performed upon physicians’ diagnoses based on observing their electrocardiogram (ECG) signals, which are not always the same. Therefore, a high-accuracy automatic method can help minimize this gap regarding AP localization. This study was to develop a novel semi-automatic localization of AP in patients with WPW syndrome, using features selected from the cross-recurrence plot (CRP) of consecutive precordial leads on ECG. The study participants comprised of 31 patients with WPW syndrome (aged 8-69, with the mean age of 31.19±14.69, 32.3% female), receiving successful ablation therapy during the first session. The features extracted from the CRP, including laminarity (LAM), trapping time (TT), determinism (DET), and mean length of diagonal line (L) were then analyzed. The feature reduction, The classification and the cross-validation (CV) methods were sequential forward selection (SFS), the k-nearest neighbors (KNN) and the leave-one-out (LOO) respectively. The proposed method could differentiate the right and left APs in the patients with WPW syndrome with the accuracy value of 87% (sensitivity: 93.33%, specificity: 81.25%). These results were achieved by the LAM and L features from the CRP of (V1, V2) and (V3, V4), respectively.
تعیین محل راه فرعی بصورت غیر تهاجمی و نیمه خودکار با استفاده از سیگنال ECG انجام شد.
ویژگیLAM حاصل از CRPی لیدهای V1 و V2 ویژگی موثری در تعیین محل راه فرعی بود.
ویژگی L حاصل از CRPی لیدهای V3 و V4 ویژگی موثری در تعیین محل راه فرعی بود.
تعیین محل راه‌های فرعی راست و چپ با صحت 87% حاصل شد.
[1] C. M. Teixeira, T. A. Pereira, A. M. Lebreiro and S. A. Carvalho, “Accuracy of the electrocardiogram in localizing the accessory pathway in patients with Wolff-Parkinson-White pattern,” Arq. Bras. Cardiol., vol. 107, no. 4, pp. 331–338, 2016, doi: 10.5935/abc.20160132.
[2] D. J. Fox, G. J. Klein, A. C. Skanes, L. J. Gula, R. Yee and A. D. Krahn, “How to identify the location of an accessory pathway by the 12-lead ECG,” Heart Rhythm, vol. 5, no. 12, pp. 1763–1766, 2008, doi: 10.1016/j.hrthm.2008.09.012.
[3] S. C. Sporton, M. J. Earley, A. W. Nathan and R. J. Schilling, “Electroanatomic versus fluoroscopic mapping for catheter ablation procedures: a prospective randomized study,” J. Cardiovasc. Electrophysiol., vol. 15, no. 3, pp. 310–315, 2004, doi: 10.1111/j.1540-8167.2004.03356.x.
[4] P. Iturralde, V. Araya-Gomez, L. Colin, S. Kershenovich, A. de Micheli and J. A. Gonzalez-Hermosillo, “A new ECG algorithm for the localization of accessory pathways using only the polarity of the QRS complex,” J. Electrocardiol., vol. 29, no. 4, pp. 289–299, 1996, doi: 10.1016/S0022-0736(96)80093-8.
[5] S. Yahyazadeh, N. Jafarnia, A. Motie and A. Ghorbani, “Biomedical Signal Processing and Control A novel feature extraction method for the localization of accessory pathways in patients with Wolff-Parkinson-White syndrome,” Biomed. Signal Process. Control, vol. 88, no. PA, p. 105640, 2024, doi: 10.1016/j.bspc.2023.105640.
[6] M. S. Arruda et al., “Development and validation of an ECG algorithm for identifying accessory pathway ablation site in Wolff‐Parkinson‐White syndrome,” J. Cardiovasc. Electrophysiol., vol. 9, no. 1, pp. 2–12, 1998, doi: 10.1111/j.1540-8167.1998.tb00861.x.
[7] R. E. Gregg, S. H. Zhou and A. M. Dubin, “Automated detection of ventricular pre-excitation in pediatric 12-lead ECG,” J. Electrocardiol., vol. 49, no. 1, pp. 37–41, 2016, doi: 10.1016/j.jelectrocard.2015.08.006.
[8] A. P. Fitzpatrick, R. P. Gonzales, M. D. Lesh, G. W. Modin, R. J. Lee and M. M. Scheinman, “New Algorithm for the Localization of Accessory AtrIoventricalar Connections Using a Baseline Electrocardiogram,” JACC, vol. 23, no. 1, pp. 107–116, 1994,doi: 10.1016/0735-1097(94)90508-8.
[9] B. Xie et al., “Localization of accessory pathways from the 12-lead electrocardiogram using a new algorithm,” Am. J. Cardiol., vol. 74, no. 2, pp. 161–165, 1994, doi: 10.1016/0002-9149(94)90090-6.
[10] C.-E. Chiang et al., “An accurate stepwise electrocardiographic algorithm for localization of accessory pathways in patients with Wolff-Parkinson-White syndrome from a comprehensive analysis of delta waves and R/S ratio during sinus rhythm,” Am. J. Cardiol., vol. 76, no. 1–2, pp. 40–46, 1995, doi: 10.1016/S0002-9149(99)80798-X.
[11] M. Nakano et al., “Estimation of the accessory pathway location of the manifest Wolff-Parkinson-White syndrome using synthesized right-sided chest leads,” J. Interv. Card. Electrophysiol., vol. 59, no. 1, pp. 43–48, 2020, doi: 10.1007/s10840-019-00648-2.
[12] T. Pambrun et al., “Maximal Pre-Excitation Based Algorithm for Localization of Manifest Accessory Pathways in Adults,” JACC Clin. Electrophysiol., vol. 4, no. 8, pp. 1052–1061, 2018, doi: 10.1016/j.jacep.2018.03.018.
[13] P. Pascale et al., “The ‘ double transition ’: a novel electrocardiogram sign to discriminate posteroseptal accessory pathways ablated from the right endocardium from those requiring a left-sided or epicardial coronary venous approach,” Eur. Soc. Cardiol., vol. 0, pp. 1–9, 2020, doi: 10.1093/europace/euaa200.
[14] A. Meyers, M. Buqammaz and H. Yang, “Cross-recurrence analysis for pattern matching of multidimensional physiological signals,” Chaos, vol. 30, no. 12, 2020, doi: 10.1063/5.0030838.
[15] N. Marwan, M. C. Romano, M. Thiel and J. Kurths, “Recurrence plots for the analysis of complex systems,” Phys. Rep., vol. 438, no. 5–6, pp. 237–329, 2007, doi: 10.1016/j.physrep.2006.11.001.
[16] N. Marwan, M. Thiel, N. R. Nowaczyk, N. Marwan, M. Thiel and N. R. Nowaczyk, “Cross Recurrence Plot Based Synchronization of Time Series,” Nonlinear Process. Geophys., no. 1040, pp. 101–107, doi: 10.5194/npg-9-325-2002.
[17] M. Calderón-Juárez, I. B. Cruz-Vega, G. H. González-Gómez and C. Lerma, “Nonlinear Dynamics of Heart Rate Variability after Acutely Induced Myocardial Ischemia by Percutaneous Transluminal Coronary Angioplasty,” Entropy, vol. 25, no. 3, 2023, doi: 10.3390/e25030469.
[18] M. Aceves-fernandez, “Methodology Proposal of ADHD Classification of Children based on Cross Recurrence Plots,” Nonlinear Dyn., vol. 104, pp. 1491-1505, 2021, doi: 10.21203/rs.3.rs-163507/v1.
[19] A. Khosla, P. Khandnor and T. Chand, “A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach,” Concurr. Comput. Pract. Exp., vol. 34, no. 13, p. e6912, 2022, doi: 10.1002/cpe.6912.
[20] S. Chandrasekharan, J. E. Jacob, A. Cherian and T. Iype, “Exploring recurrence quantification analysis and fractal dimension algorithms for diagnosis of encephalopathy,” Cogn. Neurodyn., pp. 1–14, 2023, doi: 10.1007/s11571-023-09929-z.
[21] G. Luongo et al., “Non-Invasive Identification of Atrial Fibrillation Driver Location Using the 12-lead ECG: Pulmonary Vein Rotors vs. other Locations,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, 2020, pp. 410–413, doi: 10.1109/EMBC44109.2020.9176135.
[22] S. Saraswat, G. Srivastava and S. Shukla, “Wavelet transform based feature extraction and classification of atrial fibrillation arrhythmia,” Biomed. Pharmacol. J., vol. 10, no. 4, pp. 1715–1725, 2017, doi: 10.13005/bpj/1284.
[23] P. Maury, Q. Voglimacci-Stephanopoli, F. Mandel, P. Parlier, M. Beneyto and A. Rollin, “High-density mapping of atrial insertion of right lateral retrograde decremental accessory pathway: 3D illustration of accessory atrioventricular conduction network,” Hear. Case Reports, vol. 8, no. 2, pp. 114–118, 2022, doi: 10.1016/j.hrcr.2021.11.010.
[24] L. Mahidhar and A. Kumari, “Case Teaching of MATLAB Implementation of FIR Filter with an Overview of Filter Analogies,” Int. J. Res. Eng. Sci. Manag., vol. 5, no. 12, pp. 70–75, 2022.
[25] G. Lenis, N. Pilia, A. Loewe, W. H. W. Schulze and O. Dössel, “Comparison of baseline wander removal techniques considering the preservation of ST changes in the ischemic ECG: a simulation study,” Comput. Math. Methods Med., vol. 2017, Article ID : 9295029, 2017, doi: 10.1155/2017/9295029.
[26] Y. M. Cha, G. K. Lee, K. W. Klarich and M. Grogan, “Premature ventricular contraction-induced cardiomyopathy: A treatable condition,” Circ. Arrhythmia Electrophysiol., vol. 5, no. 1, pp. 229–236, 2012, doi: 10.1161/CIRCEP.111.963348.
[27] C. L. Dye, “Atrial tachycardia in Wolff-Parkinson-White syndrome: Conversion to normal sinus rhythm with lidocaine∗,” Am. J. Cardiol., vol. 24, no. 2, pp. 265–268, 1969, doi: 10.1016/0002-9149(69)90414-7.
[28] F. A. Castaño, A. M. Hernández and G. Soto-romero, “Assessment of artifacts reduction and denoising techniques in Electrocardiographic signals using Ensemble Average-based method,” Comput. Methods Programs Biomed., vol. 182, 2019, doi: 10.1016/j.cmpb.2019.105034.
[29] S. Schinkel, N. Marwan and J. Kurths, “Order patterns recurrence plots in the analysis of ERP data,” Cogn. Neurodyn., vol. 1, no. 4, pp. 317–325, 2007, doi: 10.1007/s11571-007-9023-z.
[30] S. Li and A. Adelmann, “Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators,” Phys. Rev. Accel. Beams, vol. 26, no. 2, p. 24801, 2022, doi: 10.1103/PhysRevAccelBeams.26.024801.
[31] B. M. Mathunjwa, Y. Lin, C. Lin, M. F. Abbod and J. Shieh, “ECG arrhythmia classification by using a recurrence plot and convolutional neural network,” Biomed. Signal Process. Control, vol. 64, p. 102262, 2021, doi: 10.1016/j.bspc.2020.102262.
[32] E. Tzinis, G. Paraskevopoulos, C. Baziotis and A. Potamianos, “Integrating Recurrence Dynamics for Speech Emotion Recognition,” arXiv, pp. 1–5, 2018, doi: 10.48550/arXiv.1811.04133.
[33] S. Parvaneh, M. -R. Hashemi-Golpayegani, M. Firoozabadi and M. Haghjoo, “Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space,” J. Eng. Med., vol. 226, no. 1, pp. 3–20, 2011, doi:10.1177/0954411911425839.
[34] V. Gupta, “A novel method of cardiac arrhythmia detection in electrocardiogram signal,” nt. J. Medical Engineering and Informatics, vol. 12, no. 5, pp. 489–499, 2020, doi: 10.1504/IJMEI.2020.109943.
[35] S. K. Nayak, A. Bit, A. Dey, B. Mohapatra and K. Pal, “A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal,” J. Healthc. Eng., vol. 2018, 2018, doi: 10.1155/2018/6920420.
[36] R. Carvajal, N. Wessel, M. Vallverdú, P. Caminal and A. Voss, “Correlation dimension analysis of heart rate variability in patients with dilated cardiomyopathy,” Comput. Methods Programs Biomed., vol. 78, no. 2, pp. 133–140, 2005, doi: 10.1016/j.cmpb.2005.01.004.
[37] N. Marwan, A. Groth and J. Kurths, “Quantification of order patterns recurrence plots of event related potentials”, Chaos and Complexity Letters, vol. 2, no. 2, pp. 301–314, 2007.
[38] N. Marwan and A. Meinke, “Extended recurrence plot analysis and its application to ERP data,” Int. J. Bifurc. Chaos, vol. 14, no. 02, pp. 761–771, 2004, doi: 10.1142/S0218127404009454.
[39] A. G. S. Fatemeh Mohammadi, A. Sheikhani and F. Razzazi, “Non-invasive localization of the ectopic foci of focal atrial tachycardia by using ECG signal based sparse decomposition algorithm,” Biomed. Signal Process. Control, vol. 71, no. Part B, p. 103179, 2022, doi: 10.1016/j.bspc.2021.103014.
[40] A. d’Avila, J. Brugada, V. Skeberis, E. Andries, E. Sosa and P. Brugada, “A fast and reliable algorithm to localize accessory pathways based on the polarity of the QRS complex on the surface ECG during sinus rhythm,” Pacing Clin. Electrophysiol., vol. 18, no. 9, pp. 1615–1627, 1995, doi: 10.1111/j.1540-8159.1995.tb06983.x.
[41] A. P. Fitzpatrick et al., “New algorithm for the localization of accessory atrioventricular connections using a baseline electrocardiogram,” J. Am. Coll. Cardiol., vol. 23, no. 1, pp. 107–116, 1994, doi: 10.1111/j.1540-8159.1995.tb06983.x.
[42] R. Liu and J. Chen, “Pre-excitation syndrome with a change in terminal QRS vector,” Acta Cardiol., vol. 5385, no. May, pp. 219–221, 2017, doi: 10.1080/AC.68.2.2967285.
[43] L. Boersma, E. GARCÍA‐MORAN, L. Mont and J. Brugada, “Accessory pathway localization by QRS polarity in children with Wolff‐Parkinson‐White syndrome,” J. Cardiovasc. Electrophysiol., vol. 13, no. 12, pp. 1222–1226, 2002, doi: 10.1046/j.1540-8167.2002.01222.x.
[44] J. J. R. Kuncheva and I. Ludmila, “On feature selection protocols for very low-sample-size data,” Pattern reccognition, 2018, doi: 10.1016/j.patcog.2018.03.012.
[45] T. Senoner et al., “Identifying the location of an accessory pathway in pre-excitation syndromes using an artificial intelligence-based algorithm,” J. Clin. Med., vol. 10, no. 19, 2021., doi: 10.3390/jcm10194394.
[46] F. J. Neumann et al., “2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes,” Eur. Heart J., vol. 41, no. 3, pp. 407–477, 2020, doi: 10.1093/eurheartj/ehz425.
[47] N. Taguchi et al., “A simple algorithm for localizing accessory pathways in patients with Wolff‐Parkinson‐White syndrome using only the R/S ratio,” J. Arrhythmia, vol. 30, no. 6, pp. 439–443, 2014, doi: 10.1016/j.joa.2013.10.006.
[48] D. P. Zipes, P. Libby, R. O. Bonow, D. L. Mann and G. F. Tomaselli, Braunwald’s Heart disease, 2018.
[49] J. A. Drezner, “Standardised criteria for ECG interpretation in athletes : a practical tool,” Br. J. Sports Med., vol. 46, pp. 6–9, 2012, doi: 10.1136/bjsports-2012-091703.
[50] A. Szfadoust and H. Momenzadeh-Haghighi, “Improved SNR in Location with UWB Using Power Control Algorithms,” Journal of Southern Communication Engineering, vol. 9, no. 33, pp. 69-74, 2019 (in persian).
[51] N. Cheraghi-Shirazi, R. Hamzehyan and A. Masoomi, “The Comparison of Classification Algorithms for Remote Sensing Images,” Journal of Southern Communication Engineering, vol. 5, no. 17, pp. 31-38, 2015 (in persian).
JOURNAL OF Southern COMMUNICATION ENGINEERING |
E-ISSN: 2980-9231 https://jce.bushehr.iau.ir |
https://doi.org/10.30495/jce.2023.1983943.1199 |
Vol. 13/ No. 52/Summer 2024
Research Article |
Sakineh Yahyazadeh Saravi, Ph.D candidate1 
| Nader Jafarnia Dabanloo, Assistant Professor2* | Ali Motie Nasrabadi, Professor 3 | Alireza Ghorbani Sharif, MD, Cardiologist and Interventional Electrophysiologist4
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran, yahyazadehs@yahoo.com
2Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran, jafarnia@srbiau.ac.ir
3Department of Biomedical Engineering, Shahed University, Tehran, Iran, nasrabadi@shahed.ac.ir
4The Arrhythmia Clinic Dr. Ghorbani Sharif, Tehran, Iran, ghorbanisharif@iranep.net
Correspondence Nader Jafarnia Dabanloo, Assistant Professor, |
Received: 17 April 2023 Revised: 11 August 2023 Accepted: 27 December 2023
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Keywords: Wolff-Parkinson-White syndrome, Localization, Accessory pathway, Cross-recurrence plot.
Highlights
· The localization of APs with a semi-automatic approach using ECG signal was achieved non-invasively.
· The feature LAM yielded from the CRP of leads V1 and V2 was effective in the localization of APs.
· The feature L yielded from the CRP of leads V3 and V4 was effective in the localization of APs.
· The localization of right- and left-sided APs with LAM and L had an accuracy of 87%.