ECG data compression techniques-a unified approach
01 Apr 1990-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 37, Iss: 4, pp 329-343
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. >
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03 Nov 1994
TL;DR: A new transformation method as an electrocardiogram (ECG) data compression using a biorthogonal basis in the restoring procedure to overcome shortcomings in the existing transformation methods.
Abstract: The authors propose a new transformation method as an electrocardiogram (ECG) data compression. They use a biorthogonal basis in the restoring procedure to overcome shortcomings in the existing transformation methods. Using the biorthogonal basis, nonuniformly selected original amplitude can be used in the restoring procedure instead of the transformation coefficients of conventional methods. Flexibility in selection of amplitude samples will be useful to produce a reasonable ECG data compression depending on the demand of a cardiologist.
1 citations
26 Oct 2011
TL;DR: An innovative and energy-efficient Pre-Diagnosing ECG Transmission Technique for BASN is presented, designed to classify the sensed ECG data into the three classes of abnormal heart beats, unknown heart beats and normal heart beats so that communication energy can be saved without affecting the cardiac disease monitoring and diagnosis.
Abstract: Electrocardiograms (ECG) provide invaluable insight into the conditions of the heart and are widely used for diagnosing cardiac diseases. Recent advances in miniature sensors and low-power wireless transmitters make body area sensor networks (BASN) a compelling platform for mobile ECG monitoring. However, energy efficiency is still one of the major issues in BASN, which are typically battery-powered. In this paper, we present an innovative and energy-efficient Pre-Diagnosing ECG Transmission Technique for BASN. In our technique, we explore the differences of ECG data in terms of its importance for medical diagnosis. A self-learning ECG classification algorithm is designed to classify the sensed ECG data into the three classes of abnormal heart beats, unknown heart beats and normal heart beats. Subsequently, the communication resources are allocated differently on these heart beat classes so that communication energy can be saved without affecting the cardiac disease monitoring and diagnosis. According to our test results, about 80% to 100% classification accuracy can be achieved, with 0% misses in abnormal heart beats, while saving about 76% of energy compared with non-classifying transmission techniques in transmitting normal heart beats.
1 citations
18 Apr 2019
TL;DR: The experimental results show that the joint use of Linear Predictive Coding and Lempel-Ziv-Welch is an adequate lossless approach, and the amplitude scaling followed by the Discrete Wavelet Transform achieves the best compression ratio, with a small distortion, among the lossy techniques.
Abstract: The compression of Electrocardiography (ECG) signals acquired in off-the-person scenarios requires methods that cope with noise and other impairments on the acquisition process. In this paper, after a brief review of common on-the-person ECG signal compression algorithms, we propose and evaluate techniques for this compression task with off-the-person acquired signals, in both lossy and lossless scenarios, evaluated with standard metrics. Our experimental results show that the joint use of Linear Predictive Coding and Lempel-Ziv-Welch is an adequate lossless approach, and the amplitude scaling followed by the Discrete Wavelet Transform achieves the best compression ratio, with a small distortion, among the lossy techniques.
1 citations
Cites methods from "ECG data compression techniques-a u..."
...Among these operations, we have: amplitude scaling, Differential Pulse Code Modulation (DPCM), Amplitude Zone Time Epoch Coding (AZTEC), Turning Point (TP), and Coordinate Reduction Time Encoding Scheme (CORTES) [4], [7]....
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01 Jan 2007
TL;DR: This paper develops a DCT compression algorithm for ECG, based on optimal bits allocation, and shows that this method improve compression performances in term of compression ratio (CR) and the error percent ratio (PRD).
Abstract: Transform compression consists to apply an orthogonal transform on a window of the signal, and then proceed to reduce the number of bits representing transform coefficients. The simplest transform compression algorithm is one which eliminate the transform coefficients under threshold fixed a priori, this kind of algorithm is known as zonal transform compression system [5]. Applying this algorithm on ECG provides us a compression ratio of three (CR=3) approximately with an error ratio of five percent (PRD=5%) [3] [6]. In this paper, we develop a DCT compression algorithm for ECG, based on optimal bits allocation. The test results show that this method improve compression performances in term of compression ratio (CR) and the error percent ratio (PRD).
1 citations
01 Jan 2013
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References
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TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Abstract: In this final installment of the paper we consider the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now. To a considerable extent the continuous case can be obtained through a limiting process from the discrete case by dividing the continuum of messages and signals into a large but finite number of small regions and calculating the various parameters involved on a discrete basis. As the size of the regions is decreased these parameters in general approach as limits the proper values for the continuous case. There are, however, a few new effects that appear and also a general change of emphasis in the direction of specialization of the general results to particular cases.
65,425 citations
01 Sep 1952
TL;DR: A minimum-redundancy code is one constructed in such a way that the average number of coding digits per message is minimized.
Abstract: An optimum method of coding an ensemble of messages consisting of a finite number of members is developed. A minimum-redundancy code is one constructed in such a way that the average number of coding digits per message is minimized.
5,221 citations
01 Apr 1975
TL;DR: This paper gives an exposition of linear prediction in the analysis of discrete signals as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal.
Abstract: This paper gives an exposition of linear prediction in the analysis of discrete signals The signal is modeled as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal In the frequency domain, this is equivalent to modeling the signal spectrum by a pole-zero spectrum The major part of the paper is devoted to all-pole models The model parameters are obtained by a least squares analysis in the time domain Two methods result, depending on whether the signal is assumed to be stationary or nonstationary The same results are then derived in the frequency domain The resulting spectral matching formulation allows for the modeling of selected portions of a spectrum, for arbitrary spectral shaping in the frequency domain, and for the modeling of continuous as well as discrete spectra This also leads to a discussion of the advantages and disadvantages of the least squares error criterion A spectral interpretation is given to the normalized minimum prediction error Applications of the normalized error are given, including the determination of an "optimal" number of poles The use of linear prediction in data compression is reviewed For purposes of transmission, particular attention is given to the quantization and encoding of the reflection (or partial correlation) coefficients Finally, a brief introduction to pole-zero modeling is given
4,206 citations
TL;DR: The state of the art in data compression is arithmetic coding, not the better-known Huffman method, which gives greater compression, is faster for adaptive models, and clearly separates the model from the channel encoding.
Abstract: The state of the art in data compression is arithmetic coding, not the better-known Huffman method. Arithmetic coding gives greater compression, is faster for adaptive models, and clearly separates the model from the channel encoding.
3,188 citations
12 Apr 1976
TL;DR: The utility and effectiveness of these transforms are evaluated in terms of some standard performance criteria such as computational complexity, variance distribution, mean-square error, correlated rms error, rate distortion, data compression, classification error, and digital hardware realization.
Abstract: A tutorial-review paper on discrete orthogonal transforms and their applications in digital signal and image (both monochrome and color) processing is presented. Various transforms such as discrete Fourier, discrete cosine, Walsh-Hadamard, slant, Haar, discrete linear basis, Hadamard-Haar, rapid, lower triangular, generalized Haar, slant Haar and Karhunen-Loeve are defined and developed. Pertinent properties of these transforms such as power spectra, cyclic and dyadic convolution and correlation are outlined. Efficient algorithms for fast implementation of these transforms based on matrix partitioning or matrix factoring are presented. The application of these transforms in speech and image processing, spectral analysis, digital filtering (linear, nonlinear, optimal and suboptimal), nonlinear systems analysis, spectrography, digital holography, industrial testing, spectrometric imaging, feature selection, and patter recognition is presented. The utility and effectiveness of these transforms are evaluated in terms of some standard performance criteria such as computational complexity, variance distribution, mean-square error, correlated rms error, rate distortion, data compression, classification error, and digital hardware realization.
928 citations