Open Access
Research Article
Non-stationary wavelet for ECG signal classification
Abdelmalik Boussaada.boussaad2001@gmail.comLaboratory of Applied Mathematics University of Biskra 145 RPBiskra, 07000, AlgeriaView full profile → , Khaled Melkemikmelkemi@yahoo.frEDPA Laboratory University of Batna 2 53 Constantine RoadBatna, 05000, AlgeriaView full profile → , Farid Melganimelgani@disi.unitn.itDepartment of Information Engineering and Computer Science University of Trento Via Sommarive Povo Trento, 9 - 38123, ItalyView full profile → , *Zouhir MokhtariCorresponding authorz.mokhtari@univ-biskra.dzLaboratory of Applied Mathematics University of Biskra 145 RPBiskra, 07000, AlgeriaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 May 2021
- Accepted:
- 02 Feb 2022
- Published Online:
- 15 May 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1128
- Pages:
- 607–623
Abstract
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.
Keywords
Subject Classifications
[2010] 65T6068T0574P1042C40
References
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