Automatic Detection of P Wave in ECG During Ventricular Extrasystoles

  • Lucie MaršánováEmail author
  • Andrea Němcová
  • Radovan Smíšek
  • Tomáš Goldmann
  • Martin Vítek
  • Lukáš Smital
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 68/2)


This work introduces a new method for P wave detection in ECG signals during ventricular extrasystoles. The authors of previous works which deal with detection of P waves tested their algorithms mainly on physiological records (sinus rhythm) and they reached good results for these records. Testing of P wave detection algorithms using pathological records is usually not provided and if it is, the results are notably worse than in the case of physiological records. The automatic and reliable detection of atrial activity in pathological situations is still an unsolved problem. In this work, phasor transform in combination with classification algorithm is used for P wave detection. Phasor transform converts each ECG sample into a phasor which enhances changes in the ECG signal. The classification is based on extraction of morphological features which are derived from each QRS complex. The results of classification are used for demarcation of areas in which P waves are searched using phasor transform. The proposed algorithm was tested on signals no. 106, 119, 214 and 223 from MIT-BIH arrhythmia database, in which the ventricular extrasystoles are present. For validation whether the algorithm is functional also for signals with physiological rhythm, it was tested on the signals no. 100, 101, 103, 117, and 122. The accuracy of the P wave detection in signals with ventricular extrasystoles is Se = 98.94% and PP = 98.30% and in signals without pathology is Se = 98.47% and PP = 99.99%.


ECG Electrocardiogram Ventricular extrasystoles P wave P wave detection Pathological ECG signal 


  1. 1.
    Smíšek, R., Maršánová, L., Němcová, A., Vítek, M.; Kozumplík, J.; Nováková, M. CSE database: extended annotations and new recommendations for ECG software testing. Medical and Biological Engineering and Computing, 54(12), (2016).Google Scholar
  2. 2.
    Maršánová, L., Ronzhina, M., Smíšek, R., Vítek, M., Němcová, A., Smital, L., Nováková, M. ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study. Scientific Reports (7), 1–11 (2017).Google Scholar
  3. 3.
    Martínez, J. P., R. Almeida, S. Olmos, et al. A Wavelet-Based ECG Delineator: Evaluation on Standard Databases. IEEE Transactions on Biomedical Engineering 51(4), 570–581 (2004).Google Scholar
  4. 4.
    Martínez, A., R. Alcaraz A J. J. Rieta. Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiological Measurement 31(11), 1467–1485 (2010).Google Scholar
  5. 5.
    Maršánová, L. Detection of P, QRS and T Components of ECG Using Phasor Transform. In: IEEE Student Branch Conference, 55–58 (2016).Google Scholar
  6. 6.
    Vítek, M., Hrubeš, J.; Kozumplík, J. A Wavelet-Based ECG Delineation in Multilead ECG Signals: Evaluation on the CSE Database. In: World Congress on Medical Physics and Biomedical Engineering 177–180 (2009).Google Scholar
  7. 7.
    M Goldberger, A.L., Lan, A., Glass L., Hausdorff J.M., Ivanov P.Ch., Mark R.G., Mietus J.E., Moody G.B., Peng C.K., Stanley H.E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), 215–220 (2000).Google Scholar
  8. 8.
    Portet, F. P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts. Physiological Measurement 29(1), 141–155 (2008).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Communication, Department of Biomedical EngineeringBrno University of TechnologyBrnoCzech Republic
  2. 2.Institute of Scientific Instruments, The Czech Academy of SciencesBrnoCzech Republic
  3. 3.Faculty of Information Technology, Department of Intelligent SystemsBrno University of TechnologyBrnoCzech Republic

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