Combined coding and adaptive thresholding algorithms for ECG compression

Alshamali, Ahmad; Ghaith, Tareq

doi:10.1109/ieeegcc.2009.5734305

Cited by 4 publications

(8 citation statements)

References 8 publications

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“…Table IV shows the result of the second experiment. This result indicates that the performance of the proposed method also better than the other four methods [19], [20], [21], and [3]. The mean of PRD of the proposed method at the similar (not exact) compression rate is lower than those other four methods.…”

Section: Performance Comparison With Other Methodsmentioning

confidence: 77%

“…At the range of 11~13 compression rates, the mean of PRD of the proposed method is 2.49, which is significantly lower than the other methods, i.e. 3.8, 3.51, 3.73, and 3.57, proposed by [19], [20], [21], and [3], respectively. The same fact occurred at the range of 14~16 compression rates, the mean of PRD of the proposed method is 2.82, while the other methods generate higher distortion, i.e.…”

Section: Performance Comparison With Other Methodsmentioning

confidence: 83%

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Beat reordering for optimal electrocardiogram signal compression using SPIHT

Isa

Jatmiko

Arymurthy

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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An effective electrocardiogram (ECG) signal compression method based on two-dimensional wavelet transform which employs set partitioning in hierarchical trees (SPIHT) and beat reordering technique is presented. This method utilizes the redundancy between adjacent samples and adjacent beats. Beat reordering rearranges beat order in 2D ECG array based on the similarity between adjacent beats. This rearrangement reduces variances between adjacent beats so that the 2D ECG array contains less high frequency component. The experiments on two datasets from MIT-BIH arrhythmia database revealed that the proposed method is more efficient for ECG signal compression in comparison with several previous proposed methods in literature. The experimental results show that the proposed method yields relatively low distortion at high compression rate. ECG compression; set partitioning in hierarchical trees (SPIHT); wavelet transform; multirate signal processing I.

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Section: Performance Comparison With Other Methodsmentioning

confidence: 77%

Section: Performance Comparison With Other Methodsmentioning

confidence: 83%

Beat reordering for optimal electrocardiogram signal compression using SPIHT

Isa

Jatmiko

Arymurthy

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

show abstract

“…Table 4 shows the result of the second experiment. This result indicates that the performance of the proposed method also better than the other four methods [18], [25], [26], and [4]. The mean of PRD of the proposed method at the similar (not exact) compression rate is lower than those other four methods.…”

Section: A the Optimal Wavelet Basismentioning

confidence: 77%

“…SPIHT encoder also can be terminated at any time and provide the best representation of the signal achievable within the framework [18].…”

Section: ф(T) Is the Scaling Function ψ(T) Is The Wavelet Functionsmentioning

confidence: 99%