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Method to Correct Artifacts in Multilead ECG Using Signal Entropy

  • Beatriz Rodríguez-Alvarez
  • José R. Ledea-Vargas
  • Fernando E. Valdés-Pérez
  • Renato Peña-Cabrera
  • José-R. Malleuve-Palancar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Artifacts should be corrected previous heart rate variability analysis. A new method for artifact correction in multilead ECG is proposed in this paper. The method detects artifacts in the RR series, takes the corresponding segment of the multilead ECG, uses entropy of the signal for selecting the “cleanest” ECG channel, and uses the wavelet transform to recalculate positions of R peaks. The method was evaluated with ECG records of arrhythmia database MIT/BIH, with good results.

Keywords

ECG artifact correction entropy wavelet multilead ECG 

References

  1. 1.
    García González, M.A.: Estudio de la variabilidad del ritmo cardíaco mediante técnicas estadísticas, espectrales y no lineales. Tesis doctoral para la obtención del título de doctor. Departamento de Ingeniería Electrónica. Universidad Politécnica de Cataluña (1998)Google Scholar
  2. 2.
    Lemire, D., Pharand, C., Rajaonah, J.-C., Dubé, B., LeBlanc, A.-R.: Wavelet Time Entropy, T wave morphology and myocardial ischemia. IEEE Transactions in Biomedical Engineering 47(7), 967–970 (2000)CrossRefGoogle Scholar
  3. 3.
    Malik, M., et al.: Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology. European Heart. Journal 17, 354–381 (1996)CrossRefGoogle Scholar
  4. 4.
    Boqiang, H, Yuanyuan, W: Detecting QRS Complexes of Two-channel ECG Signals by Using Combined Wavelet Entropy. In: IEEE 3rd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2009, June 11-13, pp. 1-4 (2009) E-ISBN: 978-1-4244-2902-8a Google Scholar
  5. 5.
    Mehta, S.S., Lingayat, N.S.: Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM. Computers in Biology and Medicine 38, 138–145 (2008)CrossRefGoogle Scholar
  6. 6.
    Bermúdez, A.N., Spinelli, E.M., Muravchik, C.M.: Detección de eventos en señales de EEG mediante Entropía Espectral. XVIII Congreso Argentino de Bioingeniería SABI 2011 - VII Jornadas de Ingeniería Clínica, Mar del Plata, 28 al 30 de Septiembre (2011)Google Scholar
  7. 7.
    Chui, C.K.: Wavelets: a tutorial in theory and applications. Academic Press (1992)Google Scholar
  8. 8.
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal. and Machine Intell. 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  9. 9.
    Köhler, B., Hennig, C., Orglmeister, R.: The Principles of Software QRS Detection. IEEE Engineering in Medicine and Biology, 42–57 (February 2002)Google Scholar
  10. 10.
    Mahmoodabadi, S.Z., Ahmadian, A., Abolhasani, M.D.: ECG feature extraction using daubechies wavelets. In: Proceedings of the Fifth IASTED International Conference Visualization, Imaging and Image Processing, Benidorn, Spain, September 7-9, pp. 343–348 (2005)Google Scholar
  11. 11.
    Vera, O.E., Duque-Cardona, E., Rivera-Piedrahita, J.: Extracción de características de la señal electrocardiográfica mediante software de análisis matemático. Scientia Et Technica, Universidad Tecnológica de Pereira, Colombia, vol. XII(31), pp. 59–64 (Agosto 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Beatriz Rodríguez-Alvarez
    • 1
  • José R. Ledea-Vargas
    • 1
  • Fernando E. Valdés-Pérez
    • 1
  • Renato Peña-Cabrera
    • 2
  • José-R. Malleuve-Palancar
    • 3
  1. 1.Center for Neurosciences Studies, Images and Signals Processing (CENPIS)Universidad de OrienteCuba
  2. 2.Biomedical Engineering DepartmentUniversidad de OrienteCuba
  3. 3.Cardiological ServiceS. Lora HospitalSantiago de CubaCuba

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