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ECG CLASSIFICATION USING SLANTLET TRANSFORM AND ARTIFICIAl NEURAL ARTIFICIAL NETWORK | ||
| Journal of Engineering | ||
| Article 1, Volume 16, Issue 1, 2010, Pages 0-0 | ||
| Authors | ||
| Rasha Thabit; Tarik Zeyad; Sadiq J. Abou-Loukh | ||
| Abstract | ||
| Automatic detection and classification of cardiac arrhythmias is important for diagnosis of cardiac abnormality. This paper shows a method to accurately classify ECG arrhythmias through a combination of slantlet transform and artificial neural network (ANN). The ability of the slantlet transform to decompose signal at various resolutions allows accurate extraction of features from non-stationary signals like ECG. The low frequency coefficients, which contain the maximum information about the arrhythmia, were selected from the slantlet decomposition. These coefficients are fed to a Multi-Layer Perceptron (MLP) artificial neural network which classifies the arrhythmias. In the present work the ECG data is taken from standard MIT-BIH database. The proposed system is capable of distinguishing the normal sinus rhythm and nine different arrhythmias. The overall accuracy of classification of the proposed approach is 98.40 %. Three other transformation methods are used and the accuracy of the classification of each was compared with the slantlet system accuracy. These transformation methods are: the Fourier transform which gives 67.80% accuracy, the discrete cosine transform which gives 92.72% accuracy, and the wavelet transform (using Haar and Daubechies-4 scaling function coefficients, which give an accuracies of 96.02% and 96.25% respectively). | ||
| Keywords | ||
| Slantlet Transform; neural networks; ECG Features Extraction and Classification | ||
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