Wavelet-based ECG signals compression using SPIHT technique and VKTP coder

Boukhennoufa, N.; Benmahammed, K.; Abdi, M.A.; Djeffal, F.

doi:10.1109/icscs.2009.5412584

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…In this section the result of several experiments of the proposed method are compared with other ECG compression algorithms already realized [12][13][14][15][16][17][18][19][20][21]. In addition, the ECG signals used should be sampled with the same sampling rate and each sample is quantized by the same number of bits.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

Abo‐Zahhad¹,

Abdelhamid²,

Mohamed³

2014

IJCNS

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This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal; where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed compression algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. Illustrative examples are given to demonstrate thresholding based on energy packing efficiency strategy, coding of DWT coefficients and data packetizing. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of 10 seconds of data extracted from records 100 and 117 of MIT-BIH database. The obtained results revealed that the proposed technique possesses higher compression ratios and lower PRD compared to the other wavelet transformation techniques. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples; and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Thus, from Equation (6), the compression ratio is 17.76 CR = . Reconstructing the ECG signal from the header and the binary streams representing the RoI and NonRoI parts follows reverse processes.…”

Section: -Bitsmentioning

confidence: 99%

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

Abo‐Zahhad¹,

Abdelhamid²,

Mohamed³

2014

IJCNS

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show abstract

“…Em Boukhennoufa [53] é utilizado o método SPIHT (Set Partitioning in Hierarchical Trees) que é um método similar ao EZW, porém ordena cada nível de decomposição wavelet em uma árvore onde existirá uma relação pai-filho entre coeficientes. Nesse trabalho também se utiliza o VKTP (Vector K-Tree Partitioning), que é um método de codificação sem perdas para codificar entropia.…”

Section: Métodos Existentes De Compressão De Ecgunclassified

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

Campitelli¹

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ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques

Singhai

Kumar

Ateek

et al. 2023

Circuits Syst Signal Process

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Wavelet-based ECG signals compression using SPIHT technique and VKTP coder

Cited by 8 publications

References 10 publications

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

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

ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques

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