Beta wavelet based ECG signal compression using lossless encoding with modified thresholding

Kumar, Ranjeet; Kumar, Anil; Pandey, Rajesh K.

doi:10.1016/j.compeleceng.2012.04.008

Cited by 89 publications

(53 citation statements)

References 31 publications

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“…To evaluate the compression performances, three parameters are widely used [16][17][18][19][20] The values used in the PRD evaluation are the values where the baseline is zero. This offers a real analysis of the proposed method.…”

Section: B Analysis Of the Quantitative Resultsmentioning

confidence: 99%

“…Table 2 presents the different results of the proposed method in the analysed ECG signals, as well as different results of other techniques, namely, R. Kumar [16], X. Wang [17][18] as reported in [16], and B. Huang [19], J. Chen [20] as reported in [19]. In bought the highest and the lowest α parameters, the proposed method shows competitive results comparing to [16][17][18][19][20] and it shows the best quality score of 6,02.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…It decomposes the signal over the different level of the high pass and low pass filters [16]. Several coefficients have been developed for large choices among different scales and translations in order to obtain different sorts of high pass and low pass filters.…”

Section: A the Discrete Wavelet Transformmentioning

confidence: 99%

“…This method is based on the decomposition of the ECG signal, the thresholding stage and the encoding of the final data. The proposed method is tested on some of the MIT-BIH arrhythmia signals from the international database Physionet [16]. This paper is organized as follows, after the introduction; the next section presents the different steps of the proposed method from the decomposing of the ECG signal to the encoding of data.…”

Section: Introductionmentioning

confidence: 99%

“…This offers a high compression rate of the original signal comparing the lossless methods. Among lossy methods, the algorithms based on the discrete wavelet transform (DWT) [16][17] offer an important solution for the ECG compression. These algorithms provide a better localization of the different features consisting the ECG signal.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

B’charri¹,

Latif²,

Jenkal³

et al. 2016

ijacsa

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Abstract-The storage capacity of the ECG records presents an important issue in the medical practices. These data could contain hours of recording, which needs a large space for storage to save these records. The compression of the ECG signal is widely used to deal with this issue. The problem with this process is the possibility of losing some important features of the ECG signal. This loss could influence negatively the analyzing of the heart condition. In this paper, we shall propose an efficient method of the ECG signal compression using the discrete wavelet transform and the run length encoding. This method is based on the decomposition of the ECG signal, the thresholding stage and the encoding of the final data. This method is tested on some of the MIT-BIH arrhythmia signals from the international database Physionet. This method shows high performances comparing to other methods recently published.

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Section: B Analysis Of the Quantitative Resultsmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: A the Discrete Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

B’charri¹,

Latif²,

Jenkal³

et al. 2016

ijacsa

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Hybrid compression of biomedical ECG and EEG signals based on differential clustering and encoding techniques

Angeline

Suja

2020

Int J Imaging Syst Tech

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Signal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite where it is important that the original and the decompressed data should be identical or where deviations from the original data would lead to catastrophic consequences, especially in biomedical signal analysis and diagnostics. For which, the input signal preprocessed with differential pulse code modulation (DPCM) reduces the interchannel dependencies to get the desired output. A whole array of unique compression techniques are being utilized in the compression process. The combination of (K‐means clustering, arithmetic encoding [AE], Huffman encoding [HE]) clustering and coding compression techniques are analyzed using electro cardiogram (ECG) and electroencephalogram (EEG) signals. The proposed method employs k‐means clustering combined with Huffman encoding (DiKHE) and k‐means clustering combined with arithmetic encoding (DiKAE) individually. Compression ratio (CR) is analyzed with these combinations of compression techniques for various cluster size K (K = 2,3,4,5,6). A maximum CR of 6.03144 and 4.54126 is obtained for ECG and EEG signals respectively. The compressions based on these techniques are efficient since the compressed signal is reconstructed perfectly as it matches exactly with the original signal.

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Compression of ECG Signal Using Hybrid Technique

Surekha¹,

Patil²

2015

Studies in Computational Intelligence

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Beta wavelet based ECG signal compression using lossless encoding with modified thresholding

Cited by 89 publications

References 31 publications

The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

Hybrid compression of biomedical ECG and EEG signals based on differential clustering and encoding techniques

Compression of ECG Signal Using Hybrid Technique

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