Electrocardiogram signal compression using singular coefficient truncation and wavelet coefficient coding

Kumar, Ranjeet; Kumar, Anil; Singh, Girish Kumar

doi:10.1049/iet-smt.2015.0150

Cited by 23 publications

(8 citation statements)

References 35 publications

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“…For the purpose of fidelity assessment, PRD is exploited as a measure of reconstruction quality of ECG signals as per globally accepted standard [24–26, 31]. In the proposed method, two different temporal modelling schemes such as IP and zero‐padding have been used for 2D array construction of ECG data which improve the correlation among beats or rows in the matrix.…”

Section: Resultsmentioning

confidence: 99%

“…In the proposed algorithm, the pre‐processing stage consists of several steps: mean removal, amplitude normalisation, QRS detection, segmentation, period normalisation, and finally, construction of N × M array of ECG signal. A detailed discussion on mathematical modelling of these steps has been discussed in [23–26]. Literature review reflects that QRS complex of an ECG signal is the cardiac cycle that has different morphologies in different cases, states about the heart rate.…”

Section: Compression Methodologymentioning

confidence: 99%

“…After QRS detection, 2D array is constructed based on correlations between the adjacent heart beats of 1D ECG signal. Since QRS beat detection and period selection are very important for improving the correlation in 2D array, two different approaches such as interpolation (IP) and zero‐padding have been exploited [23–26]. Fig.…”

Section: Compression Methodologymentioning

confidence: 99%

“…It can be determined using SVD from data matrix X N × M . Therefore, 2D array of ECG sign of size

N \times M

is decomposed with SVD as [18, 24–26]

{bold-italicX}_{N \times M} = {bold-italicU}_{N \times N} \times \sum_{N \times M} \times V_{M \times M}^{normalT}

where

U

is the left singular orthogonal matrix, whereas V is the right singular orthogonal matrix and Σ is the diagonal singular matrix that contains singular value

σ_{1}, σ_{2}, \dots, σ_{M} . false(M < N false)

. These singular values of X are the square root of eigenvalue of covariance matrix

{\hat{R}}_{x} = false(1 / M false) X X^{normalT}

.…”

Section: Compression Methodologymentioning

confidence: 99%

See 3 more Smart Citations

Efficient compression technique based on temporal modelling of ECG signal using principle component analysis

Kumar

Singh

et al. 2017

IET Science, Measurement & Technology

Self Cite

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Section: Resultsmentioning

confidence: 99%

Section: Compression Methodologymentioning

confidence: 99%

Section: Compression Methodologymentioning

confidence: 99%

“…It can be determined using SVD from data matrix X N × M . Therefore, 2D array of ECG sign of size

N \times M

is decomposed with SVD as [18, 24–26]

{bold-italicX}_{N \times M} = {bold-italicU}_{N \times N} \times \sum_{N \times M} \times V_{M \times M}^{normalT}

where

U

is the left singular orthogonal matrix, whereas V is the right singular orthogonal matrix and Σ is the diagonal singular matrix that contains singular value

σ_{1}, σ_{2}, \dots, σ_{M} . false(M < N false)

. These singular values of X are the square root of eigenvalue of covariance matrix

{\hat{R}}_{x} = false(1 / M false) X X^{normalT}

.…”

Section: Compression Methodologymentioning

confidence: 99%

See 2 more Smart Citations

Efficient compression technique based on temporal modelling of ECG signal using principle component analysis

Kumar

Singh

et al. 2017

IET Science, Measurement & Technology

Self Cite

View full text Add to dashboard Cite

“…We also perform a comparative performance analysis in order to assess the effectiveness of the proposed study. For tish purpose, we consider the approach discussed by Kathirvalavakumar [36], Kamargaonkar [37], Kumar [38], and Sulthana [39]. The reason of selecting the above mentioned work is the similarity of the approach using both lossy and lossless compression scheme.…”

Section: Original Samplementioning

confidence: 99%

OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

Lakshminarayana¹,

Sarvagya²

2018

IJECE

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Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

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CARIC: A Novel Modeling of Combinatorial Approach for Radiological Image Compression

Lakshminarayana

Sarvagya

2017

Advances in Intelligent Systems and Computing

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Electrocardiogram signal compression using singular coefficient truncation and wavelet coefficient coding

Cited by 23 publications

References 35 publications

Efficient compression technique based on temporal modelling of ECG signal using principle component analysis

Efficient compression technique based on temporal modelling of ECG signal using principle component analysis

OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

CARIC: A Novel Modeling of Combinatorial Approach for Radiological Image Compression

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