Wavelet-Based Quality-Constrained ECG Data Compression System Without Decoding Process

Hsieh, Jui-Hung; Hung, King-Chü; Liu, Je-Hung; Wu, Tsung-Ching

doi:10.1109/mmul.2020.2983690

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…[28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], and [51].…”

Section: Related Workunclassified

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Darwan¹,

Anwar²,

Indra³

et al. 2023

ETJ

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The heart is one of the most important human organs. One of instruments to detect cardiac abnormalities is the electrocardiogram (ECG). This research tries to analyze ECG image in normal heart condition from ECG machine. The previous research related to the pre-processing process is the same, only at the feature extraction process look for peaks P, Q, R, S, T, heart rate, and Deviation-ST. While this research is the characteristic extraction process using wavelet transformation. The image of lead ECG 12 is processed using discrete wavelet transforms with decomposition up to ten levels, by searching for mean square error (MSE). The type of mother wavelet and the wavelet order used are Daubechies (db) with 1 (db1 (Haar)). The smallest MSE value decomposition results are obtained at the level 5, which are lead I, II, III, aVR, aVF, V4 and V5, lead V1 & V2 on level 4, for aVL (level 9), V3 (level 7) and V4 (level 6). It is expected that such research can be followed up to the identification model of cardiac abnormalities using wavelets.

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“…[28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], and [51].…”

Section: Related Workunclassified

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Darwan¹,

Anwar²,

Indra³

et al. 2023

ETJ

View full text Add to dashboard Cite

show abstract

“…Minimum loss of information and higher compression ratio are two main aspects of data compression in ECG signals [6]. Data compression methods can be categorized into two distinct categories: lossless compression and lossy compression techniques [7][8][9][10]. The lossless technique is predominantly used in biomedical signals because there is little redundancy in the data, and the signal is well preserved after compression [11,12].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Sharma

Sunkaria

2023

Biomed. Phys. Eng. Express

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Background:In telecardiology, the bio-signal acquisition processing and communication for clinical purposes occupies larger storage and significant bandwidth over a communication channel. Electrocardiograph (ECG) compression with effective reproductivity is highly desired. In the present work, a compression technique for ECG signals with less distortion by using a non-decimated stationary wavelet with a run-length encoding scheme has been proposed.Method:In the present work non-decimated stationary wavelet transform (NSWT) method has been developed to compress the ECG signals. The signal is subdivided into N levels with different thresholding values. The wavelet coefficients having values larger than the threshold are evaluated and the remaining are suppressed. In the presented technique, the biorthogonal wavelet is employed as it improves the compression ratio as well percentage root means square ratio when compared to the existing method and exhibits better results. Then the coefficients are pre-processed by using the Savitzky-Golay filter which discards the corrupted signals. The wavelet coefficients are quantized using dead-zone quantization which removes the value close to zeros. To encode these values run length encoding scheme is applied and compressed ECG signals are obtained.Results:The presented methodology has been evaluated on the MIT-BIH arrhythmias database which contains 4800 ECG fragments from forty-eight clinical records. An average compression ratio = 33.12, PRD =1.99, NPRD =2.53, and QS = 16.57 is obtained in the proposed technique.Conclusion:The proposed technique exhibits a high compression ratio and reduces distortion compared to the existing method.

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“…Several quality-guaranteed methods are available, e.g. [26][27][28] , in which the quality metric's threshold can be set to compress the signal without loss of diagnostic quality. However, this threshold can be set empirically or according to other author's recommendations which are usually determined on the whole database.…”

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confidence: 99%

Pathologies affect the performance of ECG signals compression

Němcová

Smíšek

Vítek

et al. 2021

Sci Rep

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The performance of ECG signals compression is influenced by many things. However, there is not a single study primarily focused on the possible effects of ECG pathologies on the performance of compression algorithms. This study evaluates whether the pathologies present in ECG signals affect the efficiency and quality of compression. Single-cycle fractal-based compression algorithm and compression algorithm based on combination of wavelet transform and set partitioning in hierarchical trees are used to compress 125 15-leads ECG signals from CSE database. Rhythm and morphology of these signals are newly annotated as physiological or pathological. The compression performance results are statistically evaluated. Using both compression algorithms, physiological signals are compressed with better quality than pathological signals according to 8 and 9 out of 12 quality metrics, respectively. Moreover, it was statistically proven that pathological signals were compressed with lower efficiency than physiological signals. Signals with physiological rhythm and physiological morphology were compressed with the best quality. The worst results reported the group of signals with pathological rhythm and pathological morphology. This study is the first one which deals with effects of ECG pathologies on the performance of compression algorithms. Signal-by-signal rhythm and morphology annotations (physiological/pathological) for the CSE database are newly published.

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Wavelet-Based Quality-Constrained ECG Data Compression System Without Decoding Process

Cited by 8 publications

References 14 publications

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Pathologies affect the performance of ECG signals compression

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