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Classification of ECG Arrhythmias Based on Statistical and Time-Frequency Features

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4 Author(s)
M. H. Kadbi ; Electrical Engineering Department, Sharif University of Technology, Tehran, Iran. kadbi@ee.sharif.edu ; J. Hashemi ; H. R. Mohseni ; A. Maghsoudi

In this paper a new approach to accurately classify ECG arrhythmias through a combination of the wavelet transform and artificial neural network is presented. Three kinds of features in a very computationally efficient manner are computed as follows: 1-joint Time-Frequency features (discrete wavelet transform coefficients). 2-time domain features (R-R intervals). 3-Statistical feature (Form Factor). Using these features, the limitations of other methods in classifying multiple kinds of arrhythmia with high accuracy for all of them at once are overcome. Finally, a cascade classifier including two ANNs has been designed. Considering the whole MIT-BIH arrhythmia database, 10kinds of a rrhythmia were classified. The overall accuracy of classification of the proposed approach is above 90%.

Published in:

Advances in Medical, Signal and Information Processing, 2006. MEDSIP 2006. IET 3rd International Conference On

Date of Conference:

17-19 July 2006