Predicting Cardiac Arrhythmia Using QRS Detection and Multilayer Perceptron

  • Harika Gundala
  • Mayank Sethia
  • Mehul Sethia
  • Shreyas Gonjari
  • Akshay Gugale
  • Rajeshkannan RegunathanEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 169)


Most deaths occur around the world because of cardiac disorders. Cardiac rhythm disorders may cause severe strokes and heart diseases. Arrhythmias occur when the electric signals to the heart are irregular or not working properly. Mostly, these irregular heartbeats feel like racing hearts. Many times, arrhythmias are harmless, but if they are abnormal or they result due to damaged heart, then they can be fatal. Cardiac arrhythmia, being the leading cause of death in both men and women, can be prevented with the early and correct diagnosis. In this paper, the focus is mainly on predicting whether the patient has cardiac arrhythmia or not based on electrocardiography (ECG) reports. Pan–Tompkins algorithm has been used for QRS detection which predicts the abnormal deflections that lead to the arrhythmic events. The same reports have been used to classify which type of cardiac arrhythmia the patient has using Multilayer Perceptron (MLP) algorithm.


Cardiac arrhythmia QRS detection ECG signals Pan–Tompkins algorithm Multilayer perceptron 


  1. 1.
    Arrhythmia | Irregular Heartbeat.: Retrieved from: (05 March 2019)
  2. 2.
    Arrhythmia Dataset. UCI Machine Learning Repository.
  3. 3.
    The Basics of ECG. (n.d.). Retrieved from
  4. 4.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar
  5. 5.
    Uday, B.S., Mohanalin, J., Devi, S.: ANN based prediction of cardiac arrhythmiaGoogle Scholar
  6. 6.
    Nasiri, J.A., Naghibzadeh, M., Yazdi, H.S., Naghibzadeh, B.: ECG arrhythmia classification with support vector machines and genetic algorithm. In: 2009 Third UKSim European Symposium on Computer Modeling and Simulation, IEEE, pp. 187–192 (2009)Google Scholar
  7. 7.
    Zuo, W.M., Lu, W.G., Wang, K.Q., Zhang, H.: Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier. In: 2008 Computers in Cardiology, pp. 253–256. IEEE (2008)Google Scholar
  8. 8.
    Arif, M., Akram, M.U., Afsar, F.A.: Arrhythmia beat classification using pruned fuzzy k-nearest neighbor classifier. In: 2009 International Conference of Soft Computing and Pattern Recognition, IEEE, pp. 37–42 (2009)Google Scholar
  9. 9.
    Sannino, G., De Pietro, G.: A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Futur. Gener. Comput. Syst. 86, 446–455 (2018)CrossRefGoogle Scholar
  10. 10.
    Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)CrossRefGoogle Scholar
  11. 11.
    Lu, X., Pan, M., Yu, Y.: QRS detection based on improved adaptive threshold. J. Healthc. Eng. (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Harika Gundala
    • 1
  • Mayank Sethia
    • 1
  • Mehul Sethia
    • 1
  • Shreyas Gonjari
    • 1
  • Akshay Gugale
    • 1
  • Rajeshkannan Regunathan
    • 1
    Email author
  1. 1.School of Computer Science EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations