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. >
Citations
More filters
Book•
31 Oct 1998
TL;DR: This paper presents a meta-modelling framework for Fourier Series Representations of Signal Models and Analysis-Synthesis and concludes with a comparison of these models against known models for Pitch-Synchronous Modeling.
Abstract: List of Figures. Foreword M. Vetterli. Preface. 1. Signal Models and Analysis-Synthesis. 2. Sinusoidal Modeling. 3. Multiresolution Sinusoidal Modeling. 4. Residual Modeling. 5. Pitch-Synchronous Models. 6. Matching Pursuit and Atomic Models. 7. Conclusions. Appendix A: Two-Channel Filter Banks. Appendix B: Fourier Series Representations. References. References for Poetry Excerpts. Index.
138 citations
Cites methods from "ECG data compression techniques-a u..."
...Various methods of ambulatory ECG signal compression have been presented in the literature; these rely on either the redundancy between neighboring samplings of the signal or the redundancy between adjacent periods [208, 209]....
[...]
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.
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.
138 citations
Cites methods from "ECG data compression techniques-a u..."
...In most cases, direct methods are superior to transform methods with respect to system complexity and the error control mechanism, however, transform methods usually achieve higher compression ratios and are insensitive to the noise contained in original ECG signals [1]....
[...]
...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]....
[...]
TL;DR: Wavelet Energy based diagnostic distortion (WEDD) provides a better prediction accuracy and exhibits a statistically better monotonic relationship with the MOS ratings than Wavelet based weighted percentage root mean square difference (PRD) measure (WWPRD), PRD and other objective measures.
135 citations
TL;DR: A preliminary investigation of a wavelet packet based algorithm for the compression of single lead ECG is presented, which generates significantly lower data rates with less than one-third the computational effort.
Abstract: A preliminary investigation of a wavelet packet based algorithm for the compression of single lead ECG is presented. The algorithm combines the efficiency and flexibility of wavelet packet expansions with the methodology of the Karhunen-Loeve transform (KLT). For selected records from the MIT-BIH arrhythmia database, an average data rate of 184.7 bits per second, corresponding to a compression ratio of 21.4:1, is achieved. When compared with the KLT applied to the same data, the wavelet packet algorithm generates significantly lower data rates with less than one-third the computational effort.
121 citations
14 Apr 2006
TL;DR: An introductory presentation of the basic set of algorithms used for conditioning the ECG with respect to different types of noise and artifacts, detecting heartbeats, extracting basic ECG measurements, and performing data compression is given.
Abstract: Signal processing of electrocardiographic signals has a long and rich history and has greatly helped to enhance the diagnostic capability, especially when signals are recorded in noisy environments. This article gives an introductory presentation of the basic set of algorithms used for conditioning the ECG with respect to different types of noise and artifacts, detecting heartbeats, extracting basic ECG measurements, and performing data compression. Signal processing in clinical applications is exemplified by the high-resolution ECG and T wave alternans.
121 citations
Cites methods from "ECG data compression techniques-a u..."
...AZTEC and SAPA are two well-known examples of direct methods for data compression (22,23); several variations and improvements on these two methods have been suggested over the years (24,25)....
[...]
References
More filters
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