Abstract
Accurate localization of the ectopic pacing is the key to effective catheter ablation for curing cardiac diseases such as premature ventricular contraction (PVC) and tachycardia. Invasive localization method can achieve high precision but has disadvantages of high risk, high cost, and time-consuming process, therefore, a non-invasive and convenient localization method is in demand. Noninvasive methods have been developed to utilize electrophysiological information provided by 12-lead electrocardiogram (ECG), and most of them are purely based on end-to-end data-driven architecture. This architecture generally needs a substantial and comprehensive labeled dataset, which is very difficult to obtain for whole ventricular ectopic beats in clinical setting. To address this issue, we propose a framework that combines cardiac forward-solution simulation and deep learning network for patient-specific noninvasive ectopic pacing localization. For each patient, it only requires his/her own CT images to establish a specific heart-torso model and to simulate various ECG data from different ectopic pacing locations and uses this simulated ECG data as the training dataset for our designed network. The network mainly contains time-frequency fusion module and local-global feature extraction module. Five PVC patient ECG data are tested with high precision and accuracy for ectopic pacing localization, which shows its high-potential in clinical setting.
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References
Soejima, K., Suzuki, M., Maisel, W.: Catheter ablation in patients with multiple and unstable ventricular tachycardias after myocardial infarction. Short ablation lines guided by reentry circuit isthmuses and sinus rhythm mapping. ACC Curr. J. Rev. 1(11), 66–67 (2002)
Yamada, T.: Twelve-lead electrocardiographic localization of idiopathic premature ventricular contraction origins. J. Cardiovasc. Electrophysiol. 30(11), 2603–2617 (2019)
Waxman, H.L., Josephson, M.E.: Ventricular activation during ventricular endocardial pacing: I. electrocardiographic patterns related to the site of pacing. Am. J. Cardiol. 50(1), 1–10 (1982)
Miller, J.M., Marchlinski, F.E., Buxton, A.E., Josephson, M.E.: Relationship between the 12-lead electrocardiogram during ventricular tachycardia and endocardial site of origin in patients with coronary artery disease. Circulation 77(4), 759–766 (1988)
Ito, S., et al.: Development and validation of an ECG algorithm for identifying the optimal ablation site for idiopathic ventricular outflow tract tachycardia. J. Cardiovasc. Electrophysiol. 14(12), 1280–1286 (2003)
Feng, Q., Hu, H., Liu, H.: Multi-level information for non-invasive identification of exit site of ventricular tachycardia. In: 2020 Computing in Cardiology, pp. 1–4. IEEE (2020)
Gyawali, P.K., Horacek, B.M., Sapp, J.L., Wang, L.: Learning disentangled representation from 12-lead electrograms: application in localizing the origin of ventricular tachycardia. arXiv preprint arXiv:1808.01524 (2018)
Yang, T., Yu, L., Jin, Q., Wu, L., He, B.: Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ECG. IEEE Trans. Biomed. Eng. 65(7), 1662–1671 (2017)
Monaci, S., et al.: Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices. Europace 25(2), 469–477 (2023)
Strodthoff, N., Strodthoff, C.: Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol. Meas. 40(1), 015001 (2019)
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843–852 (2017)
Gonzales, M.J., Vincent, K.P., Rappel, W.J., Narayan, S.M., McCulloch, A.D.: Structural contributions to fibrillatory rotors in a patient-derived computational model of the atria. Europace 16(suppl_4), iv3–iv10 (2014)
Villongco, C.T., Krummen, D.E., Stark, P., Omens, J.H., McCulloch, A.D.: Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block. Prog. Biophys. Mol. Biol. 115(2–3), 305–313 (2014)
Krummen, D.E., et al.: Forward-solution noninvasive computational arrhythmia mapping: the VMAP study. Circulat. Arrhythmia Electrophysiol. 15(9), e010857 (2022)
Bear, L.R., et al.: Forward problem of electrocardiography: is it solved? Circulat. Arrhythmia Electrophysiol. 8(3), 677–684 (2015)
Langrill, D.M., Roth, B.J.: The effect of plunge electrodes during electrical stimulation of cardiac tissue. IEEE Trans. Biomed. Eng. 48(10), 1207–1211 (2001)
Aliev, R.R., Panfilov, A.V.: A simple two-variable model of cardiac excitation. Chaos, Solitons Fract. 7(3), 293–301 (1996)
Minami, K.I., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46(2), 179–185 (1999)
Lee-Thorp, J., Ainslie, J., Eckstein, I., Ontanon, S.: Fnet: mixing tokens with Fourier transforms. arXiv preprint arXiv:2105.03824 (2021)
Hannun, A.Y., et al.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25(1), 65–69 (2019)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Peng, Z., et al.: Local features coupling global representations for visual recognition. In: CVF International Conference on Computer Vision, ICCV, pp. 357–366. IEEE (2021)
Van Oosterom, A., Oostendorp, T.: ECGsim: an interactive tool for studying the genesis of QRST waveforms. Heart 90(2), 165–168 (2004)
Huang, X., Yu, C., Liu, H.: Physiological model based deep learning framework for cardiac TMP recovery. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 433–443. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16434-7_42
Ramanathan, C., Ghanem, R.N., Jia, P., Ryu, K., Rudy, Y.: Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nat. Med. 10(4), 422–428 (2004)
Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (No: U1809204, 61525106) and by the Talent Program of Zhejiang Province (No: 2021R51004).
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Li, Y., Ye, H., Liu, H. (2023). Forward-Solution Aided Deep-Learning Framework for Patient-Specific Noninvasive Cardiac Ectopic Pacing Localization. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_19
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