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Journal ArticleDOI

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.
Citations
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Book ChapterDOI
01 Jan 2015
TL;DR: This paper presents synthesis of Electrocardiogram (ECG) leads from reduced set of leads using Singular Value Decomposition (SVD) to train subject-specific all desired leads for minimum of three beat periods.
Abstract: This paper presents synthesis of Electrocardiogram (ECG) leads from reduced set of leads. The Singular Value Decomposition (SVD) is used to train subject-specific all desired leads for minimum of three beat periods. Then, in the testing phase, only 3-leads are used to reconstruct all other leads. The singular value matrix of the reduced 3-lead data is transformed to a higher dimension using a transform matrix. For evaluation purpose, the proposed method is applied to a publicly available database. It contains number of 12-lead ECG recordings with different cardiac patients data. After synthesis of ECG data, the performance of the method is measured using percent correlation present between the original and synthesized data.

2 citations

Dissertation
01 Jan 2015
TL;DR: The findings from the fECG and mECG recordings at gestational periods is that there is a direct relationship between the motherÕs cardiovascular system on the foetus, it may be caused by the nutritional influence during certain gestation periods.
Abstract: During pregnancy, the fetal physiological condition is carefully checked and monitored during the foetusÕ development. It is well known that the health of the foetus relies heavily on the nutrient and oxygen supply. The oxygen and nutrient supply is exchanged from the maternal vessel to the fetal vascular system via maternal placenta. The hypothesis that this research is trying to prove is that there may exist a strong synchronised relationship between the maternal and fetal cardiac systems, and the relationship may also correlate to the gestation period. The focus of the research was to extract fECG and mECG using non-invasive abdominal recordings and analyse the relationship between the parameters derived from the ECG signals. The research used derived parameters from the abdominal recordings to analyse the relationship between the maternal and fetal cardiac systems. To do this the fECG and mECG signals need to be separated from multiple abdominal and thoracic signals from open online source recordings. The principle of the ECG separation was based on independent component analysis (ICA) that considers multiple component signals statistically independent to each other. The pairs of extracted fECG and mECG signals are analysed on the time scale to investigate the synchronisation relationship. Being able to extract the heart rate of fetus and the mother independently is the key to determining the existence of a synchronised relationship between the separate cardiac systems. The findings from the fECG and mECG recordings at di erent gestational periods is that there is a direct relationship between the motherÕs cardiovascular system on the foetus, it may be caused by the nutritional influence during certain gestation periods.

2 citations


Cites background from "ECG data compression using truncate..."

  • ...SVD takes the periodic input matrix and produces the singular vales of the matrix [118]....

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Journal ArticleDOI
TL;DR: In this paper , the authors proposed a fast and lightweight encryption for secure CS recovery outsourcing that can be used in wearable devices, such as ECG Holter monitors, instead of full recovery of CS-compressed ECG signal in the cloud, to preserve privacy, an encrypted version of ECG signals is recovered by using a randomly bipolar permuted measurement matrix.
Abstract: In the areas of communications engineering and biomedical engineering, cloud computing for storing data and running complex algorithms have been steadily increasing due to the increase in internet of things and connected health. As connected IoT devices such as wearable ECG recorders generally have less storage and computational capacity, acquired signals get sent to a remote center for storage and possible analysis on demand. Recently, compressive sensing has been used as a secure, energy-efficient and fast method of signal sampling in such recorders. In this paper, we propose a secure procedure to shift away the total recovery of compressively sensed measurement to cloud and introduce a privacy-assured signal recovery technique in the cloud. We present a fast, and lightweight encryption for secure CS recovery outsourcing that can be used in wearable devices, such as ECG Holter monitors. In the proposed technique, instead of full recovery of CS-compressed ECG signal in the cloud, to preserve privacy, an encrypted version of ECG signal is recovered by using a randomly bipolar permuted measurement matrix. The user with a key, decrypts the encrypted ECG from the cloud to obtain the original ECG signal at their end. We demonstrate our proposed method using the ECG signals available in the MIT-BIH Arrhythmia Database. We also demonstrate the strength of the proposed method against partial exposure of the key. Experimental results on client and cloud sides show our proposed method has lower complexity and consuming time compared to the recent related works, while maintaining the quality of outsourcing task in cloud.

2 citations

Proceedings ArticleDOI
04 Apr 2019
TL;DR: This paper proposes a highly scalable GPU-based parallel algorithm called GPU-DFC for computing dynamic-functional connectivity of fMRI data both at region and voxel level and exploits sparsification of correlation matrix and stores them in CSR format.
Abstract: Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer information to neuroscientists than studying the brain as a static entity. Mining of dynamic connectivity graphs from these brain studies can be used to classify diseased versus healthy brains. However, constructing and mining dynamic-functional connectivity graphs of the brain can be time consuming due to size of fMRI data. In this paper, we propose a highly scalable GPU-based parallel algorithm called GPU-DFC for computing dynamic-functional connectivity of fMRI data both at region and voxel level. Our algorithm exploits sparsification of correlation matrix and stores them in CSR format. Further reduction in the correlation matrix is achieved by parallel decomposition techniques. Our GPU-DFC algorithm achieves 2 times speed-up for computing dynamic correlations compared to state-of-the-art GPU-based techniques and more than 40 times compared to a sequential CPU version. In terms of storage, our proposed matrix decomposition technique reduces the size of correlation matrices more than 100 times. Reconstructed values from decomposed matrices show comparable results as compared to the correlations with original data. The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/GPU-DFC).

2 citations


Cites methods from "ECG data compression using truncate..."

  • ...Matrix decomposition or matrix factorization techniques like Singular Value Decomposition (SVD) are well known techniques for dimensionality reduction and compression [25], [26]....

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Proceedings ArticleDOI
01 Oct 2015
TL;DR: This paper presents synthesis of 12-lead Electrocardiogram (ECG) from a reduced set of leads using the Singular Value Decomposition (SVD) for minimum of three beat periods and the proposed method performs well for reconstruction of precordial leads in case of myocardial infarction.
Abstract: This paper presents synthesis of 12-lead Electrocardiogram (ECG) from a reduced set of leads. A patient-specific 12-lead ECG is trained using the Singular Value Decomposition (SVD) for minimum of three beat periods. Then, in the testing stage, different reduced lead sets comprising of three leads are used to reconstruct all other leads. The singular value matrix of the reduced 3-lead data is transformed to a higher dimension using a transform matrix. For evaluation purpose, the proposed method is applied to a publicly available database. It contains number of 12-lead ECG recordings with different cardiac patients' data. The proposed method performs well for reconstruction of precordial leads in case of myocardial infarction. The percent correlation exceeds 97% for most of the precordial leads. After reconstruction of 12-lead ECG data, the performance of the method is measured using three measures namely percent correlation, PRD and WEDD between the original and the synthesized data.

2 citations


Additional excerpts

  • ...The SVD [10], [11] of a matrix Xm×n is given as X = UΣV where U ∈ Rm×m, V ∈ Rn×n, and Σm×n=[diag{σ1, · · · , σr}:0], r = min(m, n), and σ1, σ2, · · · , σr are the singular values....

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References
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Book
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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Journal ArticleDOI
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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Journal ArticleDOI
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

Journal ArticleDOI
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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Journal ArticleDOI
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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