A wavelet transform-based ECG compression method guaranteeing desired signal quality

doi:10.1109/10.730435

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A wavelet transform-based ECG compression method guaranteeing desired signal quality

Journal Article•DOI•

A wavelet transform-based ECG compression method guaranteeing desired signal quality

Jie Chen¹, Shuichi Itoh•Institutions (1)

University of Electro-Communications¹

01 Dec 1998-IEEE Transactions on Biomedical Engineering (IEEE)-Vol. 45, Iss: 12, pp 1414-1419

TL;DR: A new electrocardiogram compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity are presented.

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Abstract: This paper presents a new electrocardiogram (ECG) compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity.

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Journal Article•DOI•

Wavelet transforms and the ECG: a review.

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Paul S. Addison

08 Aug 2005-Physiological Measurement

TL;DR: In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

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Abstract: The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

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794 citations

Cites methods from "A wavelet transform-based ECG compr..."

...In a later paper (Chen and Itoh 1998), again using D10 wavelets, they incorporate an adaptive quantization strategy which allows a predetermined desired signal quality to be achieved....
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...In a later paper (Chen and Itoh 1998), again using D10 wavelets, they incorporate an adaptive quantization strategy which allows a predetermined desired signal quality to be achieved. Miaou and Lin (2000) also propose a quality driven compression methodology based on Daubechies wavelets and later (Miaou and Lin 2002) on biorthogonal wavelets. The latter algorithm adopts the set partitioning of hierarchical tree (SPIHT) coding strategy. Miaou et al (2002) have also proposed a dynamic vector quantization method employing tree codevectors in a single codebook. Some examples of original and compressed signals from this work are shown in figure 27. Bradie (1996) suggested the use of a wavelet-packet-based algorithm for compression of the ECG....
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...In a later paper (Chen and Itoh 1998), again using D10 wavelets, they incorporate an adaptive quantization strategy which allows a predetermined desired signal quality to be achieved. Miaou and Lin (2000) also propose a quality driven compression methodology based on Daubechies wavelets and later (Miaou and Lin 2002) on biorthogonal wavelets. The latter algorithm adopts the set partitioning of hierarchical tree (SPIHT) coding strategy. Miaou et al (2002) have also proposed a dynamic vector quantization method employing tree codevectors in a single codebook. Some examples of original and compressed signals from this work are shown in figure 27. Bradie (1996) suggested the use of a wavelet-packet-based algorithm for compression of the ECG. When compared to the Karhunen–Loeve transform (KLT) applied to the same data the WP method generated significantly lower data rates at less than one-third the computational effort with generally excellent reconstructed signal quality. However, Blanchett et al (1998) report at least as good compression results for a KLT-based method. By first normalizing beat periods using multirate processing and normalizing beat amplitudes Ramakrishnan and Saha (1997) converted the ECG into a near cyclostationary sequence....
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...In a later paper (Chen and Itoh 1998), again using D10 wavelets, they incorporate an adaptive quantization strategy which allows a predetermined desired signal quality to be achieved. Miaou and Lin (2000) also propose a quality driven compression methodology based on Daubechies wavelets and later (Miaou and Lin 2002) on biorthogonal wavelets. The latter algorithm adopts the set partitioning of hierarchical tree (SPIHT) coding strategy. Miaou et al (2002) have also proposed a dynamic vector quantization method employing tree codevectors in a single codebook....
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...In a later paper (Chen and Itoh 1998), again using D10 wavelets, they incorporate an adaptive quantization strategy which allows a predetermined desired signal quality to be achieved. Miaou and Lin (2000) also propose a quality driven compression methodology based on Daubechies wavelets and later (Miaou and Lin 2002) on biorthogonal wavelets....
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Journal Article•DOI•

Digital coding of waveforms: Principles and applications to speech and video

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J.D. Gibson¹•Institutions (1)

Texas A&M University¹

01 Apr 1987

385 citations

Proceedings Article•DOI•

iSAX: indexing and mining terabyte sized time series

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Jin Shieh¹, Eamonn Keogh¹•Institutions (1)

University of California, Riverside¹

24 Aug 2008

TL;DR: This work shows how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature, allowing for the exact mining of truly massive real world datasets.

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Abstract: Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we show how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. Our approach allows both fast exact search and ultra fast approximate search. We show how to exploit the combination of both types of search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing millions of time series.

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375 citations

Cites background from "A wavelet transform-based ECG compr..."

...For example, in the medical domain it is frequently done for both the wavelet [5] and cosine [3] representations....
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Journal Article•DOI•

ECG signal compression using analysis by synthesis coding

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Yaniv Zigel¹, Arnon D. Cohen, Amos Katz•Institutions (1)

Ben-Gurion University of the Negev¹

01 Oct 2000-IEEE Transactions on Biomedical Engineering

TL;DR: An electrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced and was found to be superior to several well-known ECG compression algorithms at all tested bit rates.

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Abstract: An electrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean compression rate of approximately 100 bits/s (compression ratio of about 30:1) has been achieved with a good reconstructed signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As In the quantitative test, the proposed compression algorithm was found to be superior to the other tested compression algorithms.

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156 citations

Cites background or result from "A wavelet transform-based ECG compr..."

...The results in [6], [8], [10] are Fig....
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...[8], [10], because the signal was not processed to have zero...
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Journal Article•DOI•

Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure

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Amjed S. Al-Fahoum¹•Institutions (1)

Yarmouk University¹

01 Jan 2006

TL;DR: Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests.

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Abstract: Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests

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152 citations

Cites background from "A wavelet transform-based ECG compr..."

...The complexity of WDD and lack of standard code for comparison make it difficult to be adopted for quantifying a reconstructed signal’s quality....
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...Three main components should be integrated for proper performance testing: compression measure, reconstruction error, and computational complexity [6]....
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References

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Journal Article•DOI•

Compression of the ambulatory ECG by average beat subtraction and residual differencing

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P.S. Hamilton¹, Willis J. Tompkins¹•Institutions (1)

University of Wisconsin-Madison¹

01 Mar 1991-IEEE Transactions on Biomedical Engineering

TL;DR: It is demonstrated that for the low sample rate and coarse quantization required for ambulatory recording, without sufficient temporal resolution in beat location, beat subtraction does not significantly improve compression, and may even worsen compression performance.

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Abstract: A strategy is evaluated for compression of ambulatory electrocardiograms (ECGs) that uses average beat subtraction and Huffman coding of the differenced residual signal. A sample rate of 100 sps and a quantization level of 35 mu V are selected to minimize the mean-square-error distortion while maintaining a data rate that allows 24 h of two-channel ECG data to be stored in less than 4 MB of memory. With this method, sample rate, and quantization level, the ambulatory ECG is compressed and stored in real time with an average data rate of 174 b/s per channel. It is demonstrated that, for the low sample rate and coarse quantization required for ambulatory recording, without sufficient temporal resolution in beat location, beat subtraction does not significantly improve compression, and may even worsen compression performance. It is also shown that with average beat subtraction, compression is improved if multiple beat averages maintained. Improvement is most significant for ECG signals that exhibit frequent ectopic beats. >

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117 citations

Journal Article•DOI•

Data compression for storing and transmitting ECG's/VCG's

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M. E. Womble, J.S. Halliday, Sanjoy K. Mitter, Malcolm C. Lancaster, J.H. Triebwasser - Show less +1 more

01 May 1977

TL;DR: A data compression technique is given which yields a compression ratio slightly better than 12 to 1 for cardiogrmns and is implementable with either a mini- or microcomputer.

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Abstract: A data compression technique is given which yields a compression ratio slightly better than 12 to 1 for cardiogrmns and is implementable with either a mini- or microcomputer. The technique involves two applications of the discrete Karhunen-Loeve expansion, intrinsic components, principal factors, or principal components, all synonyms. The first application reduces the effects of respiration and the various orientations of different patients' hearts, and requires the solution of a 3 × 3 matrix eigenvalue, eigenvector problem for each beat. The second application involves expressing the transformed cardiogram in a Kathunen-Loeve series, and requires the solution of the eigenvalue, eigenvector problem for a large matrix. However, the solution, which must be obtained only once for all time, can be performed off line. (The same eigenvectors are used for all patients.) Comparisons are given of the cardiograms reconstructed from the compressed data with the original cardiograms.

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88 citations

Journal Article•DOI•

ECG data compression with time-warped polynomials

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Wilfried Philips

01 Nov 1993-IEEE Transactions on Biomedical Engineering

TL;DR: The author presents a new adaptive compression method for ECGs that achieves a high-quality approximation at less than 250 bits/s and the corresponding rates for other transform based schemes (the DCT and the DLT) are always higher.

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Abstract: Presents a new adaptive compression method for ECGs. The method represents each R-R interval by an optimally time-warped polynomial. It achieves a high-quality approximation at less than 250 bits/s. The author shows that the corresponding rates for other transform based schemes (the DCT and the DLT) are always higher. Also, the new method is less sensitive to errors in QRS detection and it removes more (white) noise from the signal. The reconstruction errors are distributed more uniformly in the new scheme and the peak error is usually lower. The reconstruction method is also useful for adaptive filtering of noisy ECG signals. >

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64 citations

"A wavelet transform-based ECG compr..." refers methods in this paper

...In direct methods, the compression is done directly on the ECG samples; examples include the amplitude zone time epoch coding (AZTEC), the turning point (TP), the coordinate reduction time encoding system (CORTES), the scan-along polygonal approximation (SAPA), peak-picking, cycle-to-cycle, and differential pulse code modulation (DPCM) [1]–[4]....
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Journal Article•

ECG Data Compression by Using Wavelet Transform

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Jie Chen, Shuichi Itoh, Takeshi Hashimoto

25 Dec 1993-IEICE Transactions on Information and Systems

48 citations

Journal Article•

Multiwave: A Wavelet-Based ECG Data Compression Algorithm

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Nitish V. Thakor¹, Sun Yi-chun, Hervé Rix, Pere Caminal•Institutions (1)

Johns Hopkins University¹

20 Dec 1993-IEICE Transactions on Information and Systems

TL;DR: In this article, the multiresolution wavelet technique is used for decomposing ECG signals into coarse and successively more detailed components, and the data are convolved with appropriate filters and then the alternate samples are discarded.

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Abstract: MultiWave data compression algorithm is based on the multiresolution wavelet techniqu for decomposing Electrocardiogram (ECG) signals into their coarse and successively more detailed components. At each successive resolution, or scale, the data are convolved with appropriate filters and then the alternate samples are discarded. This procedure results in a data compression rate that increased on a dyadic scale with successive wavelet resolutions. ECG signals recorded from patients with normal sinus rhythm, supraventricular tachycardia, and ventriular tachycardia are analyzed. The data compression rates and the percentage distortion levels at each resolution are obtained. The performance of the MultiWave data compression algorithm is shown to be superior to another algorithm (the Turning Point algorithm) that also carries out data reduction on a dyadic scale.

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40 citations