Classification of PVC Beat in ECG Using Basic Temporal Features

Year 2018, , 78 - 82, 30.04.2018

Yasin Kaya

https://doi.org/10.17694/bajece.419541

Cited By: 8

Abstract

Premature ventricular contraction (PVC) is
one of the most important arrhythmias among the various hearth abnormalities. Premature
depolarization of the myocardium in the ventricular region causes PVC and it is
usually associated with
structural heart conditions. Arrhythmias can be detected by examining the ECG
signal and this review requires large-size data to be examined by physicians. The
time spent by the physician in examining the signal can be reduced using CAD
systems. In this study, we propose a high performance
PVC detection system using the feature extraction and classification scheme
bringing low computational burden. The test set consisting of 81844 beats from
the MIT-BIH arrhythmia database was used for the experimental results. We
compared the performances of the various classifiers using proposed feature set
in the experiments and obtained classification accuracy of 98.71% using NN
classifier. 

Keywords

Arrhythmia, Classification, Decision Tree, Heartbeat, k-Nearest Neighbor, k-NN, Neural Network, Premature Ventricular Contraction, PVC, Support Vector Machine

References

• [1] G. K. Lee, K. W. Klarich, M. Grogan, and Y.-M. Cha, “Premature ventricular contraction-induced cardiomyopathy: a treatable condition.,” Circ. Arrhythm. Electrophysiol., vol. 5, no. 1, pp. 229–36, Feb. 2012. [2] Y. Kaya and H. Pehlivan, “Classification of Premature Ventricular Contraction in ECG,” Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 7, pp. 34–40, 2015. [3] Y. Liu, Y. Huang, J. Wang, L. Liu, and J. Luo, “Detecting Premature Ventricular Contraction in Children with Deep Learning,” J. Shanghai Jiaotong Univ., vol. 23, no. 1, pp. 66–73, Feb. 2018. [4] Y. Kaya and H. Pehlivan, “Classification of Premature Ventricular Contraction Beat Using Basic Temporal Features,” in International Advanced Researches & Engineering Congress-2017, 2017, pp. 1313–1318. [5] F. Zhou, L. Jin, and J. Dong, “Premature ventricular contraction detection combining deep neural networks and rules inference,” Artif. Intell. Med., vol. 79, pp. 42–51, Jun. 2017. [6] X. Liu, H. Du, G. Wang, S. Zhou, and H. Zhang, “Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network.,” Comput. Methods Programs Biomed., vol. 122, no. 1, pp. 47–55, Oct. 2015. [7] G. Bortolan, I. Jekova, and I. Christov, “Comparison of four methods for premature ventricular contraction and normal beat clustering,” in Computers in Cardiology, 2005, vol. 32, pp. 921–924. [8] A. Ebrahimzadeh and A. Khazaee, “Detection of premature ventricular contractions using MLP neural networks: A comparative study,” Meas. J. Int. Meas. Confed., vol. 43, pp. 103–112, 2010. [9] I. Christov, I. Jekova, and G. Bortolan, “Premature ventricular contraction classification by the K th nearest-neighbours rule,” Physiol. Meas., vol. 26, no. 1, pp. 123–130, Feb. 2005. [10] N. Z. N. Jenny, O. Faust, and W. Yu, “Automated Classification of Normal and Premature Ventricular Contractions in Electrocardiogram Signals,” J. Med. Imaging Heal. Informatics, vol. 4, no. 6, pp. 886–892, Dec. 2014. [11] M. M. Baig, H. Gholamhosseini, and M. J. Connolly, “A comprehensive survey of wearable and wireless ECG monitoring systems for older adults,” Med. Biol. Eng. Comput., vol. 51, no. 5, pp. 485–495, May 2013. [12] G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database.,” IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45–50, 2001. [13] G. Moody and R. Mark, “The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it,” in [1990] Proceedings Computers in Cardiology, 1990, pp. 185–188. [14] Y. Kaya and H. Pehlivan, “Comparison of classification algorithms in classification of ECG beats by time series,” in 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 407–410. [15] Y. Kaya, H. Pehlivan, and M. E. Tenekeci, “Effective ECG beat classification using higher order statistic features and genetic feature selection,” Biomed. Res., vol. 28, no. 17, pp. 7594–7603, 2017.