Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

  • Hamido FujitaEmail author
  • Dalibor Cimr


Arrhythmia is a disease-influencing heart and is manifested by an irregular heartbeat. Atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib) are heart arrhythmias affecting predominantly senior citizens. An electrocardiogram (ECG) is a device serving to measure the ECG signal and diagnosis of an abnormal pattern which represents a heartbeat defects. Though it is possible to analyze these signals manually, in some cases it is a difficult task due to the often signal distortion by noise. Furthermore, manual analyzation of patterns is subjective and can lead to an inaccurate diagnosis. An automated computer-aided diagnosis (CAD) is a technique to eliminate these shortcomings. In this work, we present an 6-layer deep convolutional neural network (CNN) for automatic ECG pattern classification of the normal (Nr), Afib, Afl, and Vfib classes. This proposed CNN model requires simple feature extraction and no pre-processing of ECG signals. For two seconds ECG segments, the model obtained the accuracy of 97.78%, specificity and sensitivity of 98.82% and 99.76% respectively. This proposed system can be used as an assistant automatic tool in a clinical environment as a decision support system.


Arrhythmia Atrial fibrillation Atrial flutter Convolution neural network Deep learning Electrocardiogram signals Ventricular fibrillation 



Support from the Specific Research Project “Socio-economic models and autonomous systems 2” in Faculty of Informatics and Management, University of Hradec Kralove, is gratefully acknowledged.


  1. 1.
    Abdel-Hamid O, Ar Mohamed, Jiang H, Deng L, Penn G, Yu D (2014) Convolutional neural networks for speech recognition. IEEE/ACM Trans Audio Speech Lang Process 22(10):1533–1545CrossRefGoogle Scholar
  2. 2.
    Acharya UR, Fujita H, Sudarshan VK, Sree VS, Eugene LWJ, Ghista DN, San Tan R (2015) An integrated index for detection of sudden cardiac death using discrete wavelet transform and nonlinear features. Knowl-Based Syst 83:149–158CrossRefGoogle Scholar
  3. 3.
    Acharya UR, Fujita H, Adam M, Lih OS, Hong TJ, Sudarshan VK, Koh JE (2016) Automated characterization of arrhythmias using nonlinear features from tachycardia ecg beats. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 000533–000538Google Scholar
  4. 4.
    Acharya UR, Fujita H, Sudarshan VK, Oh SL, Adam M, Koh JE, Tan JH, Ghista DN, Martis RJ, Chua CK et al (2016) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 99:146–156CrossRefGoogle Scholar
  5. 5.
    Acharya UR, Fujita H, Adam M, Lih OS, Sudarshan VK, Hong TJ, Koh JE, Hagiwara Y, Chua CK, Poo CK et al (2017) Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ecg signals: a comparative study. Inf Sci 377:17–29CrossRefGoogle Scholar
  6. 6.
    Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ecg segments with convolutional neural network. Knowl-Based Syst 132:62–71CrossRefGoogle Scholar
  7. 7.
    Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Automated detection of arrhythmias using different intervals of tachycardia ecg segments with convolutional neural network. Inf Sci 405:81–90CrossRefGoogle Scholar
  8. 8.
    Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M, Tan RS (2018) Deep convolutional neural network for the automated diagnosis of congestive heart failure using ecg signals. Appl Intell 49(1):16–27. CrossRefGoogle Scholar
  9. 9.
    Acharya UR, Fujita H, Oh SL, Raghavendra U, Tan JH, Adam M, Gertych A, Hagiwara Y (2018) Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Futur Gener Comput Syst 79:952–959CrossRefGoogle Scholar
  10. 10.
    Amiri M, Lina JM, Pizzo F, Gotman J (2016) High frequency oscillations and spikes: separating real hfos from false oscillations. Clin Neurophysiol 127(1):187–196CrossRefGoogle Scholar
  11. 11.
    Banaee H, Ahmed M, Loutfi A (2013) Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12):17472–17500CrossRefGoogle Scholar
  12. 12.
    Chow GV, Marine JE, Fleg JL (2012) Epidemiology of arrhythmias and conduction disorders in older adults. Clin Geriatr Med 28(4):539–553CrossRefGoogle Scholar
  13. 13.
    Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, Gillum RF, Kim YH, McAnulty JH, Zheng ZJ et al (2013) Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation, pp CIRCULATIONAHA–113Google Scholar
  14. 14.
    DESA U (2015) United nations department of economic and social affairs, population division. world population prospects: The 2015 revision, key findings and advance tables. Tech. rep., Working Paper No ESA/P/WP. 241Google Scholar
  15. 15.
    Desai U, Martis RJ, Acharya UR, Nayak CG, Seshikala G, SHETTY K R (2016) Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers. J Mech Med Biol 16 (01):1640005CrossRefGoogle Scholar
  16. 16.
    Fujita H, Acharya UR, Sudarshan VK, Ghista DN, Sree SV, Eugene LWJ, Koh JE (2016) Sudden cardiac death (scd) prediction based on nonlinear heart rate variability features and scd index. Appl Soft Comput 43:510–519CrossRefGoogle Scholar
  17. 17.
    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefGoogle Scholar
  18. 18.
    Hagiwara Y, Fujita H, Oh SL, Tan JH, San Tan R, Ciaccio EJ, Acharya UR (2018) Computer-aided diagnosis of atrial fibrillation based on ecg signals: a review. Inf Sci 467:99–114CrossRefGoogle Scholar
  19. 19.
    Hamed I, Owis MI (2016) Automatic arrhythmia detection using support vector machine based on discrete wavelet transform. J Med Imaging Health Inf 6(1):204–209CrossRefGoogle Scholar
  20. 20.
    January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT et al (2014) 2014 aha/acc/hrs guideline for the management of patients with atrial fibrillation: a report of the american college of cardiology/american heart association task force on practice guidelines and the heart rhythm society. J Amer Coll Cardiol 64(21):e1–e76CrossRefGoogle Scholar
  21. 21.
    Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:14085882
  22. 22.
    Lip GY, Brechin CM, Lane DA (2012) The global burden of atrial fibrillation and stroke: a systematic review of the epidemiology of atrial fibrillation in regions outside North America and Europe. Chest 142(6):1489–1498CrossRefGoogle Scholar
  23. 23.
    Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM, Suri JS (2013) Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control 8(6):888–900CrossRefGoogle Scholar
  24. 24.
    Martis RJ, Acharya UR, Adeli H (2014) Current methods in electrocardiogram characterization. Comput Biol Med 48:133–149CrossRefGoogle Scholar
  25. 25.
    Martis RJ, Acharya UR, Adeli H, Prasad H, Tan JH, Chua KC, Too CL, Yeo SWJ, Tong L (2014) Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation. Biomed Signal Process Control 13:295–305CrossRefGoogle Scholar
  26. 26.
    Moreno-Torres JG, Sáez J A, Herrera F (2012) Study on the impact of partition-induced dataset shift on k-fold cross-validation. IEEE Trans Neural Netw Learn Syst 23(8):1304–1312CrossRefGoogle Scholar
  27. 27.
    Pudukotai Dinakarrao SM, Jantsch A (2018) Addhard: Arrhythmia detection with digital hardware by learning ecg signal. In: Proceedings of the 2018 on great lakes symposium on VLSI. ACM, pp 495–498Google Scholar
  28. 28.
    Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. arXiv:14014082
  29. 29.
    Ruschitzka F, Abraham WT, Singh JP, Bax JJ, Borer JS, Brugada J, Dickstein K, Ford I, Gorcsan IIIJ, Gras D et al (2013) Cardiac-resynchronization therapy in heart failure with a narrow qrs complex. N Engl J Med 369(15):1395–1405CrossRefGoogle Scholar
  30. 30.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556
  31. 31.
    Tseng WC, Wu MH, Chen HC, Kao FY, Huang SK (2016) Ventricular fibrillation in a general population–a national database study–. Circ J 80(11):2310–2316CrossRefGoogle Scholar
  32. 32.
    Waldo AL (2017) Atrial fibrillation and atrial flutter: Two sides of the same coin!. Int J Cardiol 240:251–252CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Faculty of Information TechnologyHo Chi Minh City University of Technology (HUTECH)Ho Chi Minh CityVietnam
  2. 2.Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KraloveCzech Republic

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