High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis
| Autor: | Giuliano Grossi, Raffaella Lanzarotti, Jianyi Lin |
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| Rok vydání: | 2015 |
| Předmět: |
Lossless compression
business.industry Signal reconstruction Computer science Applied Mathematics Speech recognition Wavelet transform Pattern recognition Data_CODINGANDINFORMATIONTHEORY Sparse approximation Set partitioning in hierarchical trees Computational Theory and Mathematics Artificial Intelligence Compression (functional analysis) Signal Processing Compression ratio Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering Statistics Probability and Uncertainty business Data compression |
| Zdroj: | Digital Signal Processing. 45:96-106 |
| ISSN: | 1051-2004 |
| Popis: | Long duration recordings of ECG signals require high compression ratios, in particular when storing on portable devices. Most of the ECG compression methods in literature are based on wavelet transform while only few of them rely on sparsity promotion models. In this paper we propose a novel ECG signal compression framework based on sparse representation using a set of ECG segments as natural basis. This approach exploits the signal regularity, i.e. the repetition of common patterns, in order to achieve high compression ratio (CR). We apply k-LiMapS as fine-tuned sparsity solver algorithm guaranteeing the required signal reconstruction quality PRDN (Normalized Percentage Root-mean-square Difference). Extensive experiments have been conducted on all the 48 records of MIT-BIH Arrhythmia Database and on some 24 hour records from the Long-Term ST Database. Direct comparisons of our method with several state-of-the-art ECG compression methods (namely ARLE, Rajoub's, SPIHT, TRE) prove its effectiveness. Our method achieves average performances that are two-three times higher than those obtained by the other assessed methods. In particular the compression ratio gap between our method and the others increases with growing PRDN. |
| Databáze: | OpenAIRE |
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