Vol. 20 No. 3 (2017) Cover Image
Vol. 20 No. 3 (2017)

Published: June 30, 2017

Pages: 701-708

Articles

The Relationship between P-Wave Morphology and Atrial Fibrillation

Abstract

The objective of this paper is to develop an efficient P-wave detection algorithm based on the morphology characteristics of arrhythmias using time domain analysis.ECG from normal subjects, and patients with atrial fibrillation were studied. After baseline wander cancellation, power line interference filtration, the step of QRS detection using the pan- Tompkins algorithm is utilized to calculate R peak which represent the reference point to detect P peak.The algorithm was tested with experiments using MIT-BIH arrhythmia database which included Paroxysmal Atrial Fibrillation PAF prediction challenge, Massachusetts Institute of Technology MIT-BIH normal sinus rhythm, long term Atrial Fibrillation AF and MIT-BIH atrial fibrillation where every P-wave was extracted.The results reveal that the algorithm is accurate and efficient to detect and classify arrhythmias resulted from atrial fibrillation.

References

  1. Mendis S, Puska P, Norrving B Editors, Global Atlas on Cardiovascular Disease Prevention and Control, World Health Organization, Geneva 2011.
  2. Raija Jurkko, Atrial Electric Signal During Sinus Rhythm in Lone Paroxysmal Atrial Fibrillation, Helsinki University Central Hospital, Helsinki, Finland, 2009.
  3. Jung-Ho Heo, Sung-Woo Yang, Jung-Gwang Shin, Sun-Jung Kim, O-Kil Kim, Ji-Hyun Lee, Byung-Joo Choi, Tae-Joon Cha, and Jae-Woo Lee, Gender Differences of P Wave Signal Averaged Electrocardiograms: Based on the Risk of Atrial Fibrillation, Korean Circ. J., Vol. 37, No. 12, pp. 656, 2007.
  4. Adrian Diery, David Rowlands, Daniel A James, Tim Cutmore, Nonlinear processing techniques for P-wave Detection and Classification: A review of current methods and applications. No. 173, 2003.
  5. Sami M. Halawani, Sarudin Kari, Ibrahim AlBidewi, Ab Rahman Ahmad, ECG Simulation using Fourier Series: From Personal Computers to Mobile Devices, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 2, No. 7, pp. 1803, 2014.
  6. Adrian Diery, B. Eng, Novel Application of the Wavelet Transform For Analysis of P-waves in clinical ECG Recordings, Griffith University, Brisbane, Australia, 2008.
  7. Constanze Schmidt, Jana Kisselbach, Patrick A Schweizer, Hugo A Katus, Dierk Thomas, The pathology and treatment of cardiac arrhythmias: focus on atrial fibrillation, Dove Medical Press, Vol. 7, pp. 193, 2011.
  8. Bart P. T. Hoekstra, Probing the Dynamics of Atrial Fibrillation, An Exploration of Methods from Nonlinear Time Series Analysis, No. 12, 2000.
  9. José Joaquín Rieta, Francisco Castells, César Sánchez, Vicente Zarzoso, Atrial Activity Extraction for Atrial Fibrillation Analysis Using Blind Source Separation, IEEE Transactions on Biomedical Engineering, Vol. 51, No. 7, pp. 1176, 2004.
  10. Shreya Das, Monisha Chakraborty, Comparison of Power Spectral Density (PSD) of Normal and Abnormal ECGs, IJCA J, No. 2, pp. 10, 2011.
  11. Bartłomiej Bielecki, Marek Zieliński, Paweł Mikołajczak, ST-Elevation Myocardial Infarction simulations, Annales UMCS Informatica AI X, 2, 133-141, DOI: 10.2478/v10065-010-0059-z, 2010.
  12. Samantha POLI, Vincenzo BARBARO, Pietro Bartolini, Giovanni Calcagnini and Federica CENSI, Prediction of atrial fibrillation from surface ECG: review of methods and algorithms, Ann Ist Super Sanità, Vol. 39, No. 2, pp. 195, 2003.
  13. Teemu Vepsäläinen, Markku Laakso, Seppo Lehto, Auni Juutilainen, Juhani Airaksinen and Tapani Rönnemaa, Prolonged P wave duration predicts stroke mortality among type diabetic patients with prevalent non-major macrovascular disease, BMC Cardiovascular Disorders, 14:168, 10.1186/1471-2261-14-168, 2014.