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Journal ArticleDOI

ECG data compression techniques-a unified approach

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
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Journal ArticleDOI
TL;DR: The method is based on an orthogonal transform based on discrete Legendre polynomials that provides several advantages for compressing ECG signals when compared with conventional Fourier or cosine transforms.

13 citations

Journal ArticleDOI
TL;DR: In this paper , the authors implemented multiple data compression methods in LabVIEW with a high compression ratio and execution times below the repetition rate of the pulsed laser, and the qualitative and quantitative results of ex vivo and in vivo imaging with compression showed near-identical images to uncompressed images, with significantly smaller size.
Abstract: Photoacoustic microscopic images can assist specialists in disease diagnosis by providing vascular information. However, the size of such data is usually extremely large (ie, gigabytes), and thus, a real‐time, efficient compression method can facilitate easy storage and transportation of these images. We have implemented multiple data compression methods in LabVIEW with a high compression ratio and execution times below the repetition rate of the pulsed laser. The qualitative and quantitative results of ex vivo and in vivo imaging with compression showed near‐identical images to uncompressed images, with significantly smaller size.

13 citations

Patent
14 Sep 2010
TL;DR: In this paper, an analogue signal processor (ASP) application-specific integrated circuit (ASIC) is disclosed, which can be used for remotely monitoring ECG signals of a subject that has reduced power consumption.
Abstract: An analogue signal processor (ASP) application-specific integrated circuit (ASIC) is disclosed. The ACIS can be used for remotely monitoring ECG signals of a subject that has reduced power consumption. In one aspect, the ASIC performs the functions of: ECG signal extraction with high resolution using ECG readout channel, feature extraction using a band-power extraction channel, adaptive sampling the ECG signals using an adaptive sampling analogue-to-digital converter, and impedance monitoring for signal integrity using an impedance monitoring channel. These functions enable the development of wireless ECG monitoring systems that have significantly lower power consumption but are more efficient that predecessor systems. In one embodiment, the ASP ASIC consumes 30 μW from a 2V supply with compression provided by adaptive sampling providing large reductions in power consumption of a wireless ECG monitoring system of which the ASP ASIC forms a part.

13 citations

Proceedings ArticleDOI
18 Dec 2007
TL;DR: A new and simple target data rate (TDK) driven Wavelet-threshold based cardiac signals compression algorithm is presented for mobile telemedicine applications that is less complex because it does not require QRS detection, amplitude and period normalization and period sorting.
Abstract: One of the emerging issues in telehealth care system is how effectively the limited and well established mobile technologies that are now almost globally usable are exploited. The main challenge is to develop a mobile telemedicine system to transmit biosignals directly to a specialist in an emergency medical care unit for monitoring/diagnosis using an unmodified mobile telephone which provides the patient's information on the spot without unnecessary delays in seeking care, access to health facility and provision of adequate care at the facility. To provide a practical mobile telemedicine in GSM/GPRS/EDGE/UMTS limited capacity for transmitting the cardiac data for the diagnosis of cardiovascular diseases (CVD) which are widespread health problems with unpredictable and life-threatening consequences in most regions throughout the world, the implementation of biosignals compression technique is focused in this paper. Therefore, a new and simple target data rate (TDK) driven Wavelet-threshold based cardiac signals compression algorithm is presented for mobile telemedicine applications. The performance of the compression system is assessed in terms of compression efficiency, reconstructed signal quality and coding delay. This algorithm is tested using MIT-BIH ECG databases and qdheart PCG database records and the experimental results are compared with other Wavelet based ECG coders. The presented algorithm is less complex because it does not require QRS detection, amplitude and period normalization and period sorting.

13 citations


Cites background from "ECG data compression techniques-a u..."

  • ...are digitized at the sampling rate ranging from 100 to 1000 Hz with resolution in the range of 8 to 12 bits per sample [3]....

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Proceedings ArticleDOI
01 Sep 2018
TL;DR: Simulation results carried out on real-world ECG signals show that the proposed algorithm achieves higher compression ratios as even compared to other more complex state-of-the-art solutions.
Abstract: This paper focuses on a novel lossless compression algorithm which can be efficiently used for compression of electrocardiogram (ECG) signals. The proposed algorithm has low memory requirements and relies on a simple and efficient encoding scheme which can be implemented with elementary counting operations. Thus it can be easily implemented even in resource constrained microcontrollers as those commonly used in several low-cost ECG monitoring systems. Despite its simplicity, simulation results carried out on real-world ECG signals show that the proposed algorithm achieves higher compression ratios as even compared to other more complex state-of-the-art solutions.

13 citations


Cites background from "ECG data compression techniques-a u..."

  • ...x̂i = xi−1, but several other techniques exist ranging from higher order predictors and interpolators [19] [20] to...

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References
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
John Makhoul1
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

Journal ArticleDOI
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

Proceedings ArticleDOI
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