Electrocardiogram Compression Method Based on the Adaptive Wavelet Coefficients Quantization Combined to a Modified Two-Role Encoder

Benzid, Redha; Marir, F.; Bouguechal, Nour-Eddine

doi:10.1109/lsp.2006.887841

Cited by 52 publications

(49 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rajoub's method [22] is based on retaining coefficients given by wavelet decomposition with a required EPE (energy packing efficiency) and then compressing the coefficient 215 significance map using a variable-length encoding scheme; the wavelet decomposition is performed up to the 5th level using BiorSpline (bior4.4); thresholds were set in order to retain a 99.9% EPE for A 5 coefficients and 97% EPE for D 5 coefficients, while thresholds for level 1 to level 4 coefficients were set to retain various EPE, from 85% to 99%; retained coefficients were stored in 7 bit signed representation. Benzid et al [5] proposed another wavelet transform based method that uses a bisection algorithm to reach the user-specified PRD and the quantization of retained coef-220 ficients by TRE (two-role encoder); transformation was done up to level 5 with mother wave bior4.4, the tolerance for PRD loss due to coefficient thresholding was 1%, while the tolerance for PRD loss after quantization was 10%. Another algorithm we tested is SPIHT (set partitioning in hierarchical trees) [15], which consists in an encoder based on a set partitioning ordering defined on lists of significant wavelet coefficients that exploits the temporal orientation tree structure of the coefficients and self-similarity across different layers.…”

Section: Comparative Resultsmentioning

confidence: 99%

“…Furthermore, direct comparisons with four state-of-the-art ECG compression methods, namely ARLE [4], Rajoub [22], TRE [5], SPIHT [15], are conducted. In summary, the results show that, for low PRD values, our method achieves superior CR with respect to the competitors on three quarters of the dataset, while for higher PRD values, our method considerably outperforms 45 the others on the whole dataset.…”

mentioning

confidence: 99%

See 1 more Smart Citation

High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

Grossi

Lanzarotti

Lin

2015

Digital Signal Processing

View full text Add to dashboard Cite

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 finetuned sparsity solver algorithm guaranteeing the required signal reconstruction quality (PRD). Extensive experiments of our method and of four competitors (namely ARLE, Rajoub, SPIHT, TRE) have been conducted on all the 48 records of MIT-BIH Arrhythmia Database. Our method achieves average performances that are 3 times higher than the competitor results. In particular the compression ratio gap between our method and the others increases with the PRD growing.

show abstract

Section: Comparative Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

Grossi

Lanzarotti

Lin

2015

Digital Signal Processing

View full text Add to dashboard Cite

show abstract

“…Many researchers concentrate on wavelet based ECG compression techniques [32][33][34]. Recently, one-dimensional (1D) and twodimensional (2D) Wavelet transform based ECG compression techniques with impressive CR, low PRD and smooth signal quality are presented in literature [35][36][37][38][39][40].…”

Section: Wavelet Domainmentioning

confidence: 99%

A Review of ECG Data Compression Techniques

Singh¹,

Kaur²,

Singh³

2015

IJCA

View full text Add to dashboard Cite

Electrocardiogram (ECG) data compression reduced the storage requirements to develop a more efficient telecardiology system for cardiac analysis and diagnosis. The ECG compression without loss of diagnostic information is based on the fact that consecutive samples of the digitized ECG carry redundant information that can be removed with very less computing effort. This paper focuses on providing a comparison of the major techniques (direct, transform, parameter extraction and 2D approaches) of ECG data compression which are intended to attain a lossless compressed data with relatively high compression ratio (CR) and low percent root mean square difference (PRD).The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

show abstract

“…This implies that many of the transform coefficients will have little energy and may be discarded. A variety of encoding methods, for instance vector quantization and linear prediction, are used directly to the wavelet coefficients [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

ECG Compression using the Three-Level Quantization and Wavelet Transform

Azarbad¹,

Ebrahimzadeh²

2012

IJCA

View full text Add to dashboard Cite

The Electrocardiogram signals are a very valuable source of data for physicians in diagnosing heart abnormalities. In this paper, we present an efficient technique for compression of electrocardiogram (ECG) signals. A new thresholding method based on the three level of quantization is proposed for encoding samples using an Embedded Zero-tree Wavelet (EZW) and Huffman algorithms. The modified encoding algorithm allows an optimal data compression for a target bit rate and appeared superior to other wavelet-based ECG coders. Also, to improve the efficiency of the proposed method we propose to use different types of wavelet and compare their performances for compression of the ECG signals. Experimental results show that the proposed method has a good performance and less complexity for compression of ECG database from MIT-BIH database different types of wavelet transform.

show abstract

Electrocardiogram Compression Method Based on the Adaptive Wavelet Coefficients Quantization Combined to a Modified Two-Role Encoder

Cited by 52 publications

References 7 publications

High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

A Review of ECG Data Compression Techniques

ECG Compression using the Three-Level Quantization and Wavelet Transform

Contact Info

Product

Resources

About