ECG data compression using truncated singular value decomposition
01 Dec 2001-Vol. 5, Iss: 4, pp 290-299
TL;DR: The results showed that truncated SVD method can provide an efficient coding with high-compression ratios and demonstrated the method as an effective technique for ECG data storage or signals transmission.
Abstract: The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal information is mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that the truncated SVD method can provide efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission.
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01 Dec 2017
TL;DR: A novel algorithm is used for compressing ECG data and provides two variables: Threshold & Number of Singular Value, which can be changed to achieve optimum compression while keeping the error low.
Abstract: In this paper a novel algorithm is used for compressing ECG data The algorithm uses two techniques simultaneously to get a better compression of ECG data Firstly Data extraction: a sampling technique based on a user defined threshold value is applied on ECG followed by Truncated Singular Value Decomposition Changing the number of Singular Values higher compression can be achieved The proposed method provides two variables: Threshold & Number of Singular Value, which can be changed to achieve optimum compression while keeping the error low Compression performance & quality of reconstructed ECG in the proposed method is evaluated using Compression Ratio (CR) & Percent Root Mean Square Difference (PRD)
4 citations
09 Jun 2018
TL;DR: This paper proposes a data compression for power grid using wavelet domain singular value decomposition (WDSVD), which splits the power signal into cycles and combines them as a 2D matrix.
Abstract: This paper proposes a data compression for power grid using wavelet domain singular value decomposition (WDSVD). The method splits the power signal into cycles and combines them as a 2D matrix. Then 2D discrete wavelet transform (2DDWT) is used to decompose the original matrix into submatrices. Singular value decomposition(SVD) compresses the data in each submatrix domain according to adaptive threshold setting based on Bayesian rules. Then the data is quantized by uniform threshold quantizer. By various tests and comparisons, the new method is validated for low reconstruction error and high compression ratio (CR) of both single and multiple signal monitoring as smart grid requires.
4 citations
Cites methods from "ECG data compression using truncate..."
...Singular value decomposition (SVD) is also a popular data dimension reduction method used in ECG signals and image [20-21]....
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01 Jan 2017
4 citations
TL;DR: This article proposes a method called ‘mode pattern + mutual information’ to rank the inter-relationship between clusters, where the mode pattern is used to find outstanding objects from each cluster, and the mutual information criterion measures the close proximity of a pair of clusters.
Abstract: The evaluation of the relationships between clusters is important to identify vital unknown information in many real-life applications, such as in the fields of crime detection, evolution trees, metallurgical industry and biology engraftment. This article proposes a method called 'mode pattern + mutual information' to rank the inter-relationship between clusters. The idea of the mode pattern is used to find outstanding objects from each cluster, and the mutual information criterion measures the close proximity of a pair of clusters. Our approach is different from the conventional algorithms of classifying and clustering, because our focus is not to classify objects into different clusters, but instead, we aim to rank the inter-relationship between clusters when the clusters are given. We conducted experiments on a wide range of real-life datasets, including image data and cancer diagnosis data. The experimental results show that our algorithm is effective and promising.
4 citations
19 Dec 2014
TL;DR: A strategy that aligns each wave of all beats, and then builds a dictionary corresponding to each wave segment can distinguish each waveform of different beats and demonstrate that the proposed method can provide an effective vector quantization feature for beats classification.
Abstract: Reducing the feature dimensionality can improve the computational efficiency of electrocardiogram (ECG) beats classification system. In the long term ECG classification task, vector quantization has demonstrated its advantage in both dimensionality reduction and accuracy increase, but the existing vector quantization methods are not capable of representing the difference of each waveform among ECG beats. To make vector quantization available for ECG beats classification, in this paper, we propose a strategy that aligns each wave of all beats, and then build a dictionary corresponding to each wave segment. Thus vector quantization can distinguish each waveform of different beats. We compare our method with the popular beats features such as sampling point feature, fast Fourier transform feature, and discrete wavelet transform feature. The classification results show that our feature has high accuracy and is capable of reducing computational complexity of beats classification system, which demonstrate that the proposed method can provide an effective vector quantization feature for beats classification.
4 citations
Cites methods from "ECG data compression using truncate..."
...Then, we utilize [12] to normalize each part, which is represented by 300 uniformly distributed samples....
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...If only feature dimensionality is considered, one can use some off-theshelf dimensionality reduction techniques such as principal component analysis and independent component analysis method [12], [9], [2]....
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References
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01 Jan 1983
34,729 citations
"ECG data compression using truncate..." refers background in this paper
...Therefore, the SVD of the matrix can be performed as [20], where are the left and right singular vectors, respectively....
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TL;DR: The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
Abstract: Electrocardiogram (ECG) compression techniques are compared, and a unified view of these techniques is established. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are ECG differential pulse code modulation (DPCM) and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods include Fourier, Walsh, and Karhunen-Loeve transforms. The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods. A framework for evaluation and comparison of ECG compression schemes is presented. >
690 citations
"ECG data compression using truncate..." refers methods in this paper
...The compression techniques for an ECG have been extensively discussed [ 1 ] and can be classified into the following three major categories....
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TL;DR: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed and is significantly more efficient in compression and in computation than previously proposed ECG compression schemes.
Abstract: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm (A. Said and W.A. Pearlman, IEEE Trans. Ccts. Syst. II, vol. 6, p. 243-50, 1996) has achieved notable success in still image coding. The authors modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.
521 citations
TL;DR: Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECGs are clinically useful.
Abstract: Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.
445 citations
"ECG data compression using truncate..." refers methods in this paper
...[23]) provides a better performance than previous wavelet-based methods (Hilton [22] and Djohan et al....
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TL;DR: A preprocessing program developed for real-time monitoring of the electrocardiogram by digital computer has proved useful for rhythm analysis.
Abstract: A preprocessing program developed for real-time monitoring of the electrocardiogram by digital computer has proved useful for rhythm analysis. The program suppresses low amplitude signals, reduces the data rate by a factor of about 10, and codes the result in a form convenient for analysis.
374 citations
"ECG data compression using truncate..." refers methods in this paper
...2) Direct time-domain techniques: including amplitude zone time epoch coding (AZTEC), delta coding, and entropy coding [2]–[4]....
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