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On the application of optimal wavelet filter banks for ECG signal classification

Hadjiloucas, S., Jannah, N., Hwang, F. and Galvão, R. K. H. (2014) On the application of optimal wavelet filter banks for ECG signal classification. Journal of Physics: Conference Series, 490 (1). 012142. ISSN 1742-6588

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To link to this item DOI: 10.1088/1742-6596/490/1/012142

Abstract/Summary

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:38005
Publisher:Institute of Physics

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