Journal of Biomedical Engineering and Technology
ISSN (Print): 2373-129X ISSN (Online): 2373-1303 Website: Editor-in-chief: Ahmed Al-Jumaily
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Journal of Biomedical Engineering and Technology. 2018, 6(1), 1-6
DOI: 10.12691/jbet-6-1-1
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Cardiac Electrophysiology Studies Based on Image and Machine Learning

Yashbir Singh1, Deepa1, Shi-Yi Wu1, Michael Friebe2, João Manuel R. S. Tavares3 and Weichih Hu1,

1Department of Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taiwan

2Electrical Engineering and Information Technologies, Otto-von-Guericke-University, Magdeburg, Germany

3Instituto de Ciência e Inovação em Engenharia Mecânicae Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, PORTUGAL

Pub. Date: February 26, 2018

Cite this paper:
Yashbir Singh, Deepa, Shi-Yi Wu, Michael Friebe, João Manuel R. S. Tavares and Weichih Hu. Cardiac Electrophysiology Studies Based on Image and Machine Learning. Journal of Biomedical Engineering and Technology. 2018; 6(1):1-6. doi: 10.12691/jbet-6-1-1


Cardiac Electrophysiology study is the origin and treatment of arrhythmia, which is an abnormality in the rate, regularity or sequence of cardiac activation. There has been considerable recent development in this field, where computational methods such as Imaging and Machine Learning for Cardiac Electrophysiology, provide the framework for cardiac re-modeling. In this research, we review various recent strategies currently available for the meeting the goal of structurally and functionally integrated models of cardiac function that combine data intensive cellular systems models with compute-intensive anatomically detailed multiscale simulations.

cardiac electrophysiology cardiac re-modelling cardiac imaging machine learning

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[1]  Thornton, Andrew. “Imaging in electrophysiology.” SA Heart Journal 5.4 (2017): 166-170.
[2]  Shinbane, Jerold S., et al. “CT imaging: cardiac electrophysiology applications.” Cardiac CT imaging. Springer London, 2010. 293-308.
[3]  Pullan, Andrew, David Paterson, and Fred Greensite. “Non-invasive imaging of cardiac electrophysiology.” Philosophical Transactions: Mathematical, Physical and Engineering Sciences 2001: 1277-1286.
[4]  Panutich, Michael S., and Bradley P. Knight. “Imaging techniques in cardiac electrophysiology.” Expert review of cardiovascular therapy 4.1 (2006): 59-70.
[5]  Bengio, Yoshua, Aaron Courville, and Pascal Vincent. “Representation learning: A review and new perspectives.” IEEE transactions on pattern analysis and machine intelligence 35.8 2013: 1798-1828.
[6]  LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 2015: 436-444.
[7]  Schmidhuber, Jürgen. “Deep learning in neural networks: An overview.” Neural networks 61 2015: 85-117.
[8]  Rahman, Md Mahmudur, Prabir Bhattacharya, and Bipin C. Desai. “A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.” IEEE Transactions on Information Technology in Biomedicine 11.1 2007: 58-69.
[9]  Kononenko, I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 2001, 23(1), 89-109.
[10]  Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Group, P., “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,” Int. J. Surg., 2010, 8(5), pp. 336–341.
[11]  Berchialla P, Foltran F, Bigi R, Gregori D. Integrating stress-related ventricular functional and angiographic data in preventive cardiology: a unified approach implementing a Bayesian network. J Eval Clin Pract. 2012; 18: 637-643.
[12]  Isgum I, Prokop M, Niemeijer M, Viergever MA, van Ginneken B. Automatic coronary calcium scoring in low-dose chest computed tomography. IEEE Trans Med Imaging. 2012; 31: 2322-2334.
[13]  Lee K, Zhu J, Shum J, Zhang Y, Muluk SC, Chandra A, Eskandari MK, Finol EA. Surface curvature as a classifier of abdominal aortic aneurysms: a comparative analysis. Ann Biomed Eng. 2013; 41: 562-576.
[14]  Mohammadpour RA, Abedi SM, Bagheri S, Ghaemian A. Fuzzy rulebased classification system for assessing coronary artery disease. Comput Math Methods Med. 2015; 2015: 564867.
[15]  Xiong G, Kola D, Heo R, Elmore K, Cho I, Min JK. Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal. 2015; 24: 77-89.
[16]  Knackstedt C, Bekkers SC, Schummers G, Schreckenberg M, Muraru D, Badano LP, Franke A, Bavishi C, Omar AM, Sengupta PP. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs Multicenter Study. J Am Coll Cardiol. 2015; 66: 1456-1466.
[17]  Arsanjani R, Dey D, Khachatryan T, Shalev A, Hayes SW, Fish M, Nakanishi R, Germano G, Berman DS, Slomka P. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015; 22: 877-884.
[18]  Berikol GB, Yildiz O, Özcan IT. Diagnosis of acute coronary syndrome with a support vector machine. J Med Syst. 2016; 40: 84.
[19]  Čelutkienė J, Burneikaitė G, Petkevičius L, Balkevičienė L, Laucevičius A. Combination of single quantitative parameters into multiparametric model for ischemia detection is not superior to visual assessment during dobutamine stress echocardiography. Cardiovasc Ultrasound. 2016; 14: 13.
[20]  Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJ, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim YJ, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017; 38: 500-507.
[21]  Patel, Shailendra B., et al. “Three-dimensional rotational angiography imaging of double aortic arch vascular ring.” Images in pediatric cardiology 2013.
[22]  Sommer, Philipp, et al. “Non-fluoroscopic catheter tracking for fluoroscopy reduction in interventional electrophysiology.” Journal of visualized experiments: JoVE 99 2015.
[23]  Thornton, Andrew. “Imaging in electrophysiology.” SA Heart Journal 5.4 ,2017: 166-170.
[24]  Szili-Torok T, Kimman GJ, Scholten M, et al. Ablation lesions in Koch’s triangle assessed by three-dimensional myocardial contrast echocardiography. Cardiovasc Ultrasound.2004 Dec 9; 2(1): 27.
[25]  Jongbloed MR, Bax JJ, Lamb HJ, et al. Multislice computed tomography versus intracardiac echocardiography to evaluate the pulmonary veins before radiofrequency catheter ablation of atrial fibrillation: a head-to-head comparison. J Am Coll Cardiol. 2005 Feb 1; 45(3): 343-50.
[26]  Packer DL. Three-dimensional mapping in interventional electrophysiology: techniques and technology. J Cardiovasc Electrophysiol. 2005 Oct; 16(10): 1110-6.
[27]  Kistler PM, Earley MJ, Harris S, et al. Validation of three-dimensional cardiac image integration: use of integrated CT image into electroanatomic mapping system to perform catheter ablation of atrial fibrillation. J Cardiovasc Electrophysiol. 2006 Apr; 17(4): 341-8.
[28]  Dong J, Calkins H, Solomon SB, et al. Integrated electroanatomic mapping with three-dimensional computed tomographic images for real-time guided ablations. Circulation. 2006 Jan 17; 113(2): 186-94.
[29]  Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. Mitosis detection in breast cancer histology images with deep neural networks. In International Conference on Medical Image Computing and Computer-assisted Intervention, 2013, (pp. 411-418). Springer, Berlin, Heidelberg.
[30]  McCulloch, W. S., & Pitts, W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943, 5(4), 115-133.
[31]  Farley, B. W. A. C., & Clark, W. Simulation of self-organizing systems by digital computer. Transactions of the IRE Professional Group on Information Theory, 1954, 4(4), 76-84.
[32]  Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 1958, 65(6), 386.
[33]  LeCun, Y. Generalization and network design strategies. Connectionism in perspective, 1989, 143-155.
[34]  Krizhevsky, A., Sutskever, I., & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 2012, (pp. 1097-1105).