A Novel Method for Detection of Atrial Fibrillation Based on Heart Rate Variability

  • Akib ShahEmail author
  • Vaishali Ingale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


Atrial Fibrillation (AF) detection is one of most important part of clinical testing. We propose a novel method to detect AF episodes based on heart rate variability feature of AF. In this method, scatter plot of the heart rate is found and histogram of the Y axis data of scatter plot is used for calculations. Depending upon amount of data present in each bin of histogram ECG signal is classified as Atrial Fibrillation (AF) or Normal Sinus Rhythm (NSR). Physionet 2017 challenge database, MIT-BIH AF database and MIT-BIH NSR database are used to validate the algorithm. Physionet Challenge contains 5787 ECG records of 30/60 s classified as AF or NSR, MIT-BIH AF database contains 25 full length ECG records and MIT-BIH NSR database contains 18 full length ECG records. Using the method, we got the accuracy of 97.23% for Physionet 2017 challenge database and 97.15% for MIT-BIH AF database. MIT-BIH NSR database didn’t show any AF episode. This method can also be used for real time monitoring of ECG for AF detection.


Atrial fibrillation Heart rate variability Scatter plot Histogram 


  1. 1.
    Wolf, P.A., et al.: Atrial fibrillation as an independent risk factor for stroke. Stroke 22, 983–988 (1991)CrossRefGoogle Scholar
  2. 2.
    Benjamin, E.J., et al.: Impact of atrial fibrillation on the risk of death. Am. Heart Assoc. 98, 946–952 (1998)Google Scholar
  3. 3.
    Couceiro, R., et al.: Detection of atrial fibrillation using model-based ECG analysis. IEEE Trans. Biomed. Eng. Scholar
  4. 4.
    Du, X., et al.: A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann. Noninvasive Electrocardiol. Scholar
  5. 5.
    Alcaraz, R., et al.: Wavelet sample entropy: a new approach to predict termination of atrial fibrillation. Comput. Cardiol. 33, 597–600 (2006)Google Scholar
  6. 6.
    Ladavich, S., Ghoraani, B.: Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomed. Signal. Process. Control 18, 274–281 (2015)CrossRefGoogle Scholar
  7. 7.
    Maier, C., et al.: Screening and prediction of paroxysmal atrial fibrillation by analysis of heart rate variability parameters. Comput. Cardiol. 28, 129–132 (2001)Google Scholar
  8. 8.
    Climent, A.M., et al.: Poincare surface profiles of RR intervals: a novel noninvasive method for the evaluation of preferential AV nodal conduction during atrial fibrillation. IEEE Trans. Biomed. Eng. 56(2), 433–442 (2009)CrossRefGoogle Scholar
  9. 9.
    Tateno, K., Glass, L.: Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals. Med. Biol. Eng. Comput. 39(6), 664–671 (2001)CrossRefGoogle Scholar
  10. 10.
    Bruun, I.H., et al.: Automatic atrial fibrillation detection: a novel approach using discrete wavelet transform and heart rate variability. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, pp. 3981–3984 (2017)Google Scholar
  11. 11.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME 32(3) (1985)CrossRefGoogle Scholar
  12. 12.
    Moody, G.B., et al.: PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation (2000).
  13. 13.
    Park, et al.: Atrial fibrillation detection by heart rate variability in Poincare plot. Biomed. Eng. Online 8, 38 (2009)CrossRefGoogle Scholar
  14. 14.
    Dash, et al.: Automatic real time detection of atrial fibrillation. Ann. Biomed. Eng. 37(9), 1701–1709 (2009)CrossRefGoogle Scholar
  15. 15.
    Lobabi-Mirghavami, H., Abdali-Mohammadi, F., Fathi, A.: A novel grammar-based approach to atrial fibrillation arrhythmia detection for pervasive healthcare environments. JCS 2(2) (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and TelecommunicationsCollege of Engineering, Pune (COEP)PuneIndia

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