Yıl 2018, Cilt 6, Sayı 2, Sayfalar 10 - 14 2018-04-29

Classification of PVC Beat in ECG Using Basic Temporal Features

YASIN KAYA [1]

88 138

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. 

Arrhythmia, Classification, Decision Tree, Heartbeat, k-Nearest Neighbor, k-NN, Neural Network, Premature Ventricular Contraction, PVC, Support Vector Machine
  • [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.
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Yazar: YASIN KAYA

Bibtex @araştırma makalesi { bajece419541, journal = {Balkan Journal of Electrical and Computer Engineering}, issn = {2147-284X}, address = {Balkan Yayın}, year = {2018}, volume = {6}, pages = {10 - 14}, doi = {10.17694/bajece.419541}, title = {Classification of PVC Beat in ECG Using Basic Temporal Features}, key = {cite}, author = {KAYA, YASIN} }
APA KAYA, Y . (2018). Classification of PVC Beat in ECG Using Basic Temporal Features. Balkan Journal of Electrical and Computer Engineering, 6 (2), 10-14. DOI: 10.17694/bajece.419541
MLA KAYA, Y . "Classification of PVC Beat in ECG Using Basic Temporal Features". Balkan Journal of Electrical and Computer Engineering 6 (2018): 10-14 <http://dergipark.gov.tr/bajece/issue/36835/419541>
Chicago KAYA, Y . "Classification of PVC Beat in ECG Using Basic Temporal Features". Balkan Journal of Electrical and Computer Engineering 6 (2018): 10-14
RIS TY - JOUR T1 - Classification of PVC Beat in ECG Using Basic Temporal Features AU - YASIN KAYA Y1 - 2018 PY - 2018 N1 - doi: 10.17694/bajece.419541 DO - 10.17694/bajece.419541 T2 - Balkan Journal of Electrical and Computer Engineering JF - Journal JO - JOR SP - 10 EP - 14 VL - 6 IS - 2 SN - 2147-284X- M3 - doi: 10.17694/bajece.419541 UR - http://dx.doi.org/10.17694/bajece.419541 Y2 - 2017 ER -
EndNote %0 Balkan Journal of Electrical and Computer Engineering Classification of PVC Beat in ECG Using Basic Temporal Features %A YASIN KAYA %T Classification of PVC Beat in ECG Using Basic Temporal Features %D 2018 %J Balkan Journal of Electrical and Computer Engineering %P 2147-284X- %V 6 %N 2 %R doi: 10.17694/bajece.419541 %U 10.17694/bajece.419541
ISNAD KAYA, YASIN . "Classification of PVC Beat in ECG Using Basic Temporal Features". Balkan Journal of Electrical and Computer Engineering 6 / 2 (Nisan 2018): 10-14. http://dx.doi.org/10.17694/bajece.419541