AZTEC, a Preprocessing Program for Real-Time ECG Rhythm Analysis

Cox, Jerome R.; Nolle, Floyd M.; Fozzard, Harry A.; Oliver, George

doi:10.1109/tbme.1968.4502549

Cited by 331 publications

(95 citation statements)

References 3 publications

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“…One used the WDD measure for minimization (ASEC ) and the other used the PRD measure for minimization (ASEC ). We have also implemented the AZTEC [3] algorithm, SAPA2 [7], and LTP [12] (without entropy coding) and evaluated them with the same database signals. These compressors were chosen for comparison, because AZTEC and SAPA2 are often referred for comparison in the literature, and LTP is one of the best ECG compressors.…”

Section: Resultsmentioning

confidence: 99%

ECG signal compression using analysis by synthesis coding

Zigel

Cohen

Katz

2000

IEEE Trans. Biomed. Eng.

134

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Abstract-In this paper, an elecrocardiogram (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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Section: Resultsmentioning

confidence: 99%

ECG signal compression using analysis by synthesis coding

Zigel

Cohen

Katz

2000

IEEE Trans. Biomed. Eng.

134

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“…ECG lossy compression methods are usually classified into two main categories: direct methods and transform-based methods. Direct methods are applied in the time domain: for instance, Amplitude Zone Time Epoch Coding (AZTEC) [4] is an algorithm that converts ECG signals into a sequence of piecewise affine functions. Transform-based techniques seek for a sparse representation in a particular basis and compression is achieved by discarding coefficients with magnitude less than a certain threshold.…”

Section: Prior Work On Ecg Compression and Modelingmentioning

confidence: 99%

Finite rate of innovation based modeling and compression of ECG signals

Baechler

Freris

Quick

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Mobile health is gaining increasing importance for society and the quest for new power efficient devices sampling biosignals is becoming critical. We discuss a new scheme called Variable Pulse Width Finite Rate of Innovation (VPW-FRI) to model and compress ECG signals. This technique generalizes classical FRI estimation to enable the use of a sum of asymmetric Cauchy-based pulses for modeling electrocardiogram (ECG) signals. We experimentally show that VPW-FRI indeed models ECG signals with increased accuracy compared to current standards. In addition, we study the compression efficiency of the method: compared with various widely used compression schemes, we showcase improvements in terms of compression efficiency as well as sampling rate.

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“…In recent years, many schemes have been suggested for compression of ECG data [1][2][3][4][5][6][7][8][9][10][11]. These compression techniques can be broadly classified into three groups: (1) direct data handling techniques [1][2][3], (2) transformationbased techniques [3][4][5][6][7][8][9][10][11], and (3) parameterized modelbased techniques [9].…”

Section: Introductionmentioning

confidence: 99%

“…These compression techniques can be broadly classified into three groups: (1) direct data handling techniques [1][2][3], (2) transformationbased techniques [3][4][5][6][7][8][9][10][11], and (3) parameterized modelbased techniques [9]. Our approach belongs to the second group.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding

Abo‐Zahhad

Ahmed

Zakaria

2012

Modelling and Simulation in Engineering

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This paper presents an efficient electrocardiogram (ECG) signals compression technique based on QRS detection, estimation, and 2D DWT coefficients thresholding. Firstly, the original ECG signal is preprocessed by detecting QRS complex, then the difference between the preprocessed ECG signal and the estimated QRS-complex waveform is estimated. 2D approaches utilize the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. The error signal is cut and aligned to form a 2-D matrix, then the 2-D matrix is wavelet transformed and the resulting wavelet coefficients are segmented into groups and thresholded. There are two grouping techniques proposed to segment the DWT coefficients. The threshold level of each group of coefficients is calculated based on entropy of coefficients. The resulted thresholded DWT coefficients are coded using the coding technique given in the work by (Abo-Zahhad and Rajoub, 2002). The compression algorithm is tested for 24 different records selected from the MIT-BIH Arrhythmia Database (MIT-BIH Arrhythmia Database). The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and low computational complexity in comparison with other methods.

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AZTEC, a Preprocessing Program for Real-Time ECG Rhythm Analysis

Cited by 331 publications

References 3 publications

ECG signal compression using analysis by synthesis coding

ECG signal compression using analysis by synthesis coding

Finite rate of innovation based modeling and compression of ECG signals

An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding

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