CFP last date
22 April 2024
Reseach Article

Investigating Cardiac Arrhythmia in ECG using Random Forest Classification

by R. Ganesh kumar, Dr. Y S Kumaraswamy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 37 - Number 4
Year of Publication: 2012
Authors: R. Ganesh kumar, Dr. Y S Kumaraswamy
10.5120/4599-6557

R. Ganesh kumar, Dr. Y S Kumaraswamy . Investigating Cardiac Arrhythmia in ECG using Random Forest Classification. International Journal of Computer Applications. 37, 4 ( January 2012), 31-34. DOI=10.5120/4599-6557

@article{ 10.5120/4599-6557,
author = { R. Ganesh kumar, Dr. Y S Kumaraswamy },
title = { Investigating Cardiac Arrhythmia in ECG using Random Forest Classification },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 37 },
number = { 4 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume37/number4/4599-6557/ },
doi = { 10.5120/4599-6557 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:23:27.998071+05:30
%A R. Ganesh kumar
%A Dr. Y S Kumaraswamy
%T Investigating Cardiac Arrhythmia in ECG using Random Forest Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 37
%N 4
%P 31-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrocardiogram (ECG) is used to assess the heart arrhythmia. Accurate detection of beats helps determine different types of arrhythmia which are relevant to diagnose heart disease. Automatic assessment of arrhythmia for patients is widely studied. This paper presents an ECG classification method for arrhythmic beat classification using RR interval. The methodology is based on discrete cosine transform (DCT) conversion of RR interval. The RR interval of the beat is extracted from the ECG and used as feature. DCT conversion of RR interval is applied and the beats are classified using random tree. Experiments were conducted using MIT-BIH arrhythmia database.

References
  1. Sandoe E, Sigurd B. Arrhythmia – A Guide to Clinical Electrocardiology. Bingen: Publishing Partners Verlags GmbH, 1991.
  2. J. Pan and W. J. Tompkins, “A Real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. BME-32, no. 3, pp. 230–236, Mar. 1985.
  3. R.M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, Wiley–Interscience, New York, 2001.
  4. I. Christov, G. Gómez-Herrero, V. Krasteva, I. Jekova, A. Gotchev, K. Egiazarian, Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification, Med. Eng. Phys. 28 (2006) 876–887.
  5. P. Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng. 51 (2004) 1196–1206.
  6. A. Koski, Modelling ECG signals with Hidden Markov Models, Artif. Intell. Med. 8 (1996) 453–471.
  7. C.W. Li, C.X. Zheng, C.F. Tai, Detection of ECG characteristic points using wavelet transform, IEEE Trans. Biomed. Eng. 42 (1995) 21–28.
  8. P. Laguna, R. Jane, S. Olmos, N.V. Thakor, H. Rix, P. Caminal, Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model, Med. Biol. Eng. Comput. 34 (1996) 58–68.
  9. Z. Dokur, T. Olmez, ECG beat classification by a novel hybrid neural network, Comput. Methods Program Biomed. 66 (2001) 167–181.
  10. Y.P. Meau, F. Ibrahim, S.A.L. Narainasamy, R. Omar, Intelligent classification of ECG signal using extended EKF based neural fuzzy system, Comput. Methods Program Biomed. 82 (2006) 157–168.
  11. J. P. Martinez, S. Olmos, and P. Laguna, “Evaluation of a wavelet-based ECG waveform detector on the QT database,” Comput. Cardiol., vol. 27, pp. 81–84, Sep. 2000.
  12. P. M. Agante and J. P.Marques de Sa, “ECG noise filtering using wavelets with soft-thresholding methods,” Comput. Cardiol., vol. 26, pp. 535–538, Sep. 1999.
  13. Ahmad Khoureich Ka, ECG beats classification using waveform similarity and RR interval, Arxiv preprint arXiv:1101.1836, 2011.
  14. Tsipouras MG, Fotiadis DI, Sideris D. An arrhythmia classi?cation system based on the RR interval signal. Artif Intell Med 2005;33:237—50
  15. M. Engin, “ECG beat classification using neuro-fuzzy network,” Pattern Recognition Letters 25 (2004) 1715-1722.
  16. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220.
  17. N. Ahmed, T. Natarajan, Discrete-Time Signals and Systems, Reston Publishing Company, 1983.
  18. Frederick Livingston: Implementation of Breiman’s Random Forest Machine Learning Algorithm, inECE591Q Machine Learning conference, Fall 2005.
Index Terms

Computer Science
Information Sciences

Keywords

ECG ECG Arrhythmia classification MIT-BIH ECG data RR interval DCT Random forest