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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Classification of Electrocardiogram Signal using Multiresolution Wavelet Transform and Neural Network
verfasst von : M. F. M. Elias, H. Arof
Erschienen in: 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006
Verlag: Springer Berlin Heidelberg
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This paper discusses on the classification of electrocardiogram (ECG) signal using multiresolution wavelet transform and neural network. Multiresolution wavelet transform is used as a method of feature extraction of ECG signal since it has the ability to analyze the signal both in time and frequency domain. Neural network is used because of its ability to learn and perform classification on ECG signal. In this paper, four type of ECG signal has been chosen for classification. Based on the data obtained from MIT-BIH Arrhythmia Database the classification rate is found to be 95.08%.