Non-stationary wavelet for ECG signal classification

Wavelet analysis has shown to be an interesting tool for representing ECG signals for classification. In this paper, we present a new ECG signal representation based on the notion of non-stationary wavelets. The main difference with the construction of standard wavelets is that the multiresolution spaces are generated by scale-dependent functions in order to achieve increased flexibility and sparseness. In order to customize the non-stationary wavelet to the given ECG classification task, we resort to the fireworks optimization algorithm, thus making the proposed method general and not constrained by the choice of a particular classifier. The proposed method is validated on AAMI classes of the well-known MIT data set. Results compared to standard stationary wavelets show a significant boost in accuracy.