Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

  • Authors

    • Achmad Rizal
    • Riandini .
    • Teni Tresnawati
    2018-12-01
    https://doi.org/10.14419/ijet.v7i4.44.26975
  • Premature ventricle contraction, electrocardiogram, multilevel wavelet entropy, support vector machine, arrhythmia

  • Abstract

    One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.

     

     

  • References

    1. Brosche TAM, The EKG Handbook. Jones & Bartlett Publisher, (2010).

      [2] Ashley EA, & Niebauer J, “Conquering the ECG - Cardiology Explained - NCBI Bookshelf,†in Cardiology Explained, Remedica, (2004).

      [3] Lanatá A, Valenza G, Mancuso C, & Scilingo EP, “Robust multiple cardiac arrhythmia detection through bispectrum analysis,†Expert Syst. Appl., vol. 38, no. 6, pp. 6798–6804, (2011).

      [4] Mitra M, & Samanta RK, “Cardiac Arrhythmia Classification Using Neural Networks with Selected Features,†Procedia Technol., vol. 10, pp. 76–84, (2013).

      [5] Kaya Y, & Pehlivan H, “Classification of Premature Ventricular Contraction in ECG,†Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 7, pp. 34–40, (2015).

      [6] Rosso OA et al., “Wavelet entropy : a new tool for analysis of short duration brain electrical signals,†J. Neurosci. Methods, vol. 105, pp. 65–75, (2001).

      [7] Physionet.org, “ECG Database.†[Online]. Available: http://physionet.org/physiobank/database/#ecg.

      [8] Rizal A, & Hadiyoso S, “ECG signal classification using Hjorth Descriptor,†2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015, no. 2, pp. 87–90., (2015)

      [9] Xu A, Ye Q, Qiang J, Shan S, & Zhang D, “A wavelet time entropy algorithm for ultrasonic signal detection,†Comput. Model. New Technol., vol. 18, no. 12, pp. 69–73, (2014).

      [10] Cortes C, & Vapnik V, “Support-vector networks,†Mach. Learn., vol. 20, no. 3, pp. 273–297, (1995).

      [11] Palaniappan R, Biological Signal Analysis. Ventus Publishing ApS, (2010).

      [12] Safara S, Doraisamy S, Azman A, Jantan A, & Ranga S, “Wavelet packet entropy for heart murmurs classification,†Adv. Bioinformatics, vol. 2012, (2012).

      [13] Yıldırım O, Pławiak O, Tan TS, & Acharya UR, “Arrhythmia detection using deep convolutional neural network with long duration ECG signals,†Comput. Biol. Med., no. September, (2018).

      [14] Jenny NZN, Faust O, & Yu W, “Automated Classification of Normal and Premature Ventricular Contractions in Electrocardiogram Signals,†J. Med. Imaging Heal. Informatics, vol. 4, no. 6, pp. 886–892, (2014).

      [15] Rizal A, Hidayat R, & Nugroho HA, “Multilevel wavelet packet entropy: A new strategy for lung sound feature extraction based on wavelet entropy,†2017 International Conference on Robotics, Automation and Sciences (ICORAS), pp. 1–5., (2017)

  • Downloads

  • How to Cite

    Rizal, A., ., R., & Tresnawati, T. (2018). Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy. International Journal of Engineering & Technology, 7(4.44), 161-164. https://doi.org/10.14419/ijet.v7i4.44.26975

    Received date: 2019-02-02

    Accepted date: 2019-02-02

    Published date: 2018-12-01