Rapid Detection of Heart Rate Fragmentation and Cardiac Arrhythmias: Cycle-by-Cycle rr Analysis, Supervised Machine Learning Model and Novel Insights

  • Ananya RajagopalanEmail author
  • Marcus Vollmer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)


Heart rate dynamics are a macroscopic indicator of cardiac health. Sino-atrial degradation manifested as heart rate fragmentation (HRF) are analyzed using rr values (relative-RR intervals) derived from the inter-beat-intervals of ECGs. The rr-value is useful for the analysis of cycle-by-cycle variations such as HRF and arrhythmias. Three novel metrics developed in this work: CM20, Z3e20 and sPIP, along with two conventional metrics: SDNN and LFHF ratio are used for the detection of HRF and arrhythmias. The supervised machine learning technique of random forests is applied to develop the classification model. For this, we used a balanced dataset of 300 cases comprising of arrhythmic, non-arrhythmic coronary artery disease, and individuals without any medically significant cardiac conditions. The model was tested on 104 independent cases. The F1 score of the classifier is 91.1% without any adjustments for age, gender, prior medical conditions, etc. Insight into threshold values of heart rate dynamics for arrhythmic, heart rate fragmentation and normal cases are obtained from a single decision tree model.


Heart rate fragmentation Arrhythmia detection Random forest Machine learning 


  1. 1.
    Leading Causes of Deaths, Centers for Disease Control and Prevention (CDC).
  2. 2.
    Costa, M.D., Redline, S., Davis, R.B., Heckbert, S.R., Soliman, E.Z., Goldberger, A.L.: Heart rate fragmentation as a novel biomarker of adverse cardiovascular events: the multi-ethnic study of atherosclerosis. Front. Physiol. (2018).
  3. 3.
    PhysioNet/CinC Challenge (2017).
  4. 4.
    Thew-project, University of Rochester, Coronary Artery Patients database –, Healthy Individuals database –
  5. 5.
    Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). Scholar
  6. 6.
    Vollmer, M.: Arrhythmia classification in long-term data using relative RR intervals, Computing in Cardiology (CinC), September 2017.
  7. 7.
    Vollmer, M.: Ph.D. Dissertation, p. 63, Sect. 2.4.2.
  8. 8.
    Lin, C.C., Yang, C.-M.: Heartbeat classification using normalized rr intervals and morphological features. Math. Probl. Eng. 2014.
  9. 9.
    Schaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017). Scholar
  10. 10.
    Costa, M.D., Davis, R.B., Goldberger, A.L.: Heart rate fragmentation: a new approach to the analysis of cardiac interbeat interval dynamics. Front. Physiol. 8, 255 (2017). Scholar
  11. 11.
    Mathworks: Matlab ver. 2018a, Random Forest Tree Bagger algorithmGoogle Scholar
  12. 12.
  13. 13.
    Sanchis-Gomar, F., Perez-Quillis, C., Leischik, R., Lucia, A.: Epidemiology of coronary heart disease and acute coronary syndrome. Ann. Transl. Med. 4(13), 246 (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Redmond High SchoolRedmondUSA
  2. 2.Institute of BioinformaticsUniversity Medicine GreifswaldGreifswaldGermany

Personalised recommendations