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

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Hadjiloucas, S.^1 ; Jannah, N.^1 ; Hwang, F.^1 ; Galvão, R.K.H.^2

School of Systems Engineering, University of Reading, United Kingdom^1

Divisão de Engenharia Eletrônica, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, 12228-900, Brazil^2

关键词: Decomposition level;    Filter parameter;    Frequency selectivity;    Neural network classifier;    Numerical optimization algorithms;    Orthogonal filter banks;    Perfect reconstruction;    Signal decomposition;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012142/pdf
DOI  :  10.1088/1742-6596/490/1/012142

来源: IOP

【 摘 要 】

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.