ECG signal compression algorithm based on joint-multiresolution analysis (J-MRA)

Abstract

This paper explores the new algorithm for ECG signal compression based on Joint-multiresolution analysis (J-MRA) using Gaussian pyramid and wavelet analysis. From signal compression prospective, MRA play key role to present signal in few numbers of coefficients with its parental features. The proposed algorithm has been tested on 10 second length of 19 ECG signals from MIT-BIH Arrhythmia database and compared with recent contemporary techniques. The simulation results show that the proposed method achieves high compression ratio at relatively low distortion in comparison with other methods. Analysis contains various simulation results, where the average compression is 86.14% at 4.96% of PRD and correlation founded between original and reconstructed signal is 0.998. I.

Multiresolution Analysis of EEG Signals

“…Banerjee et al (2012) used the discrete wavelet transform to clear out noise of ECG signals and detect R-peaks by adapting the multiresolution approach. Kumar et al (2015) explored a method for ECG signal compression based on multiresolution analysis. The results showed that the proposed method achieves a high compression ratio at relatively low distortion.…”

Compressão de sinais ECG utilizando DWT com quantização não-linear e por sub-bandas

“…No trabalho de Kumar [47], para a decomposição se utiliza uma análise multi-resolução (MRA) usando uma pirâmide gaussiana, e ao resultado dessa decomposição é aplicada uma DWT. Se realiza uma limiarização do resultado e por última se usa RLE.…”