Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis

Filip Plesinger, Juraj Jurco, Josef Halamek, Pavel Leinveber, T. Reichlova, Pavel Jurak


Ultra-high-frequency ECG (UHF-ECG) in a range of 500–1,000 Hz has been tested as a new information source for analysis of left-ventricle dyssynchrony and other myocardial abnormalities. The power of UHF signals is extremely low, for which reason an averaging technique is used to improve signal-to-noise ratio. Since ventricle dyssynchrony is different for various QRS complex types, the detected QRS complexes must be clustered into morphology groups prior to averaging. Here, we present a fully-automated method for clustering. The first goal of the method is to separate previously detected QRS complexes into different morphology groups. The second goal is to precisely fit the QRS annotation marks to the exact same position against the QRS shape. The method is based on the Pearson correlation and is optimized for parallel processing. In our application with UHF-ECG data the number of detected groups was 3.24 ± 3.41 (mean and standard deviation over 1,030 records). The method can be used in other areas also where the clustering of repetitive signal formations is needed. For validation purposes, the method was tested on the MIT-BIH Arrhythmia and INCART databases from Physionet with results of purity of 98.24% and 99.50%.


  1. Abboud, S. et al., 1987. Detection of transient myocardial ischemia by computer analysis of standard and signalaveraged high-frequency electrocardiograms in patients undergoing percutaneous transluminal coronary angioplasty. Circulation, 76(3), pp.585-596.
  2. Amit, G. et al., 2013. High-frequency QRS analysis in patients with acute myocardial infarction: a preliminary study. Annals of noninvasive electrocardiology?: the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc, 18(2), pp.149-156.
  3. Breithardt, G. et al., 1991. Standards for analysis of ventricular late potentials using high-resolution or signal-averaged electrocardiography. A statement by a Task Force Committee of the European Society of Cardiology, the American Heart Association, and the American College of Ca. Circulation, 83(4), pp.1481- 1488.
  4. Castro, D. & Paulo, F., 2014. A method for context-based adaptive QRS clustering in real-time. Biomedical and Health Informatics, IEEE Journal of, PP(99), pp.1-12.
  5. Cuesta-Frau, D., Pérez-Cortés, J. C. & Andreu-García, G., 2003. Clustering of electrocardiograph signals in computer-aided Holter analysis. Computer Methods and Programs in Biomedicine, 72(3), pp.179-196.
  6. Fukunami, M. et al., 1991. Detection of patients at risk for paroxysmal atrial fibrillation during sinus rhythm by P wave-triggered signal-averaged electrocardiogram. Circulation, 83(1), pp.162-169.
  7. Goldberger, A. L. et al., 1981. Effect of myocardial infarction on high-frequency QRS potentials. Circulation, 64(1), pp.34-42.
  8. Goldberger, A. L. et al., 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23), pp.215-220.
  9. Haberl, R. et al., 1988. Comparison of frequency and time domain analysis of the signal-averaged electrocardiogram in patients with ventricular tachycardia and coronary artery disease: methodologic validation and clinical relevance. Journal of the American College of Cardiology, 12(1), pp.150-158.
  10. Hon, E. H. & Lee, S. T., 1963. Noise reduction in fetal electrocardiography. American Journal of Obstetrics & Gynecology, 87(8), pp.1086-1096. Available at:
  11. Chang, K.-C. et al., 2005. a Comparison of Similarity Measures for Clustering of Qrs Complexes. Biomedical Engineering: Applications, Basis and Communications, 17(06), pp.324-331.
  12. Jarrett, J. R. & Flowers, N. C., 1991. Electrophysiology , Pacing , and Arrhythmia Signal- Averaged Electrocardiography?: History , Techniques , and Clinical Applications. Clin. Cardiol., 14, pp.984-994.
  13. Jurak, P. et al., 2013. Ultra-high-frequency ECG Measurement Institute of Scientific Instruments , AS , Brno , Czech Republic. Computing in Cardiology Conference (CinC), 2013, 40, pp.783-786.
  14. Lagerholm, M. & Peterson, G., 2000. Clustering ECG complexes using hermite functions and self-organizing maps. IEEE Transactions on Biomedical Engineering, 47(7), pp.838-848.
  15. Moody, G. B. & Mark, R. G., 2001. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), pp.45-50.
  16. Plesinger, F. et al., 2014. Robust Multichannel QRS Detection. Computing in Cardiology Conference (CinC), 2014, 41, pp.557-560.
  17. Rompelman, O. & Ros, H. H., 1986a. Coherent averaging technique: a tutorial review. Part 1: Noise reduction and the equivalent filter. Journal of biomedical engineering, 8(1), pp.24-29.
  18. Rompelman, O. & Ros, H. H., 1986b. Coherent averaging technique: a tutorial review. Part 2: Trigger jitter, overlapping responses and non-periodic stimulation. Journal of biomedical engineering, 8(1), pp.30-35.
  19. Schlegel, T. T. et al., 2004. Real-time 12-lead highfrequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. Mayo Clinic proceedings. Mayo Clinic, 79(3), pp.339-350.
  20. Simson, M. B., 1983. Clinical application of signal averaging. Cardiol Clin, 1(1), pp.109-119. Available at:
  21. Spackman, T. N., Abel, M. D. & Schlegel, T. T., 2005. Twelve-lead high-frequency QRS electrocardiography during anesthesia in healthy subjects. Anesthesia and Analgesia, 100(4), pp.1043-1047.

Paper Citation

in Harvard Style

Plesinger F., Jurco J., Halamek J., Leinveber P., Reichlova T. and Jurak P. (2015). Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis . In Proceedings of the 3rd International Congress on Cardiovascular Technologies - Volume 1: CARDIOTECHNIX, ISBN 978-989-758-160-1, pages 11-19. DOI: 10.5220/0005604200110019

in Bibtex Style

author={Filip Plesinger and Juraj Jurco and Josef Halamek and Pavel Leinveber and T. Reichlova and Pavel Jurak},
title={Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis},
booktitle={Proceedings of the 3rd International Congress on Cardiovascular Technologies - Volume 1: CARDIOTECHNIX,},

in EndNote Style

JO - Proceedings of the 3rd International Congress on Cardiovascular Technologies - Volume 1: CARDIOTECHNIX,
TI - Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis
SN - 978-989-758-160-1
AU - Plesinger F.
AU - Jurco J.
AU - Halamek J.
AU - Leinveber P.
AU - Reichlova T.
AU - Jurak P.
PY - 2015
SP - 11
EP - 19
DO - 10.5220/0005604200110019