ECG compression by multirate processing of beats

IEEE Transactions on Biomedical Engineering, 2000

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.

IEEE Transactions on Biomedical Engineering, 1990

A broad spectrum of techniques for electrocardiogram (ECG) data compression have been proposed during the last three decades. Such techniques have been vital in reducing the digital ECG data volume for storage and transmission. These techniques are essential to a wide variety of applications ranging from diagnostic to ambulatory ECG's. Due to the diverse procedures that have been employed, comparison of ECG compression methods is a major problem. Present evaluation methods preclude any direct comparison among existing ECG compression techniques. The main purpose of this paper is to address this issue and to establish a unified view of ECG compression techniques. 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 and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods briefly presented, include: Fourier, Walsh, and K-L transforms. The theoretical basis behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, differential pulse code modulation (DPCM), and entropy coding methods. The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

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The electrical signal is generated by the heart and it is record by the electro cardiogram, for electro cardiogram (ECG) data compression has been purposed the last three decades. Such techniques have been vital in reducing the digital ECG data volume for storage and transmission. Continuous recording by ECG, So in It record is so voluminous, so it is practically do not to handle it without compression, for transmission purpose , in rural area such excellent cardiologist is not available so the data is send to other cardiologist a large data size takes many time to send, so by compression data size is reduced and take minimum time. The ECG data is compress by some technique DWT, DCT, Wavelet denoising and compression and Huffman coding the data base is taking from MIT-BIH record 104, and tested these technique on MATLAB. The DWT based algorithm gives better result to DCT based algorithm.

2014

ECG is a standard tool to monitor heart function. ECG generated waveforms are used to find patterns of irregularities in cardiac cycles in patients. In many cases, irregularities evolve over an extended period of time that requires continuous monitoring. However, this requires compression of ECG signals. In the past decades, many compression methods have been proposed. In this paper a comparative analysis of Fast Fourier Transform (FFT), discrete sine Transform (DST), and Discrete cosine Transform (DCT) based approach is carried out with good compression ratio and less computation time. To generalize transform based techniques, Tachycardia data base recording which have larger information content are compressed .The appropriate use of a block based DCT associated to a uniform scalar dead zone quantiser and arithmetic coding show very good results, confirming that the proposed strategy exhibits competitive performances compared with the most popular compressors used for ECG compression.

The paper is devoted to the ECG-dedicated compression algorithm based on the event-driven variable quantization level in three upper octaves of the time-frequency signal representation. The algorithm uses an integer-to-integer reversible wavelet transform and the segmentation procedure developed for diagnostic purpose. Our method was implemented in Matlab and tested against the world-standard databases. Although the global compression efficiency and distortion ratio are not outstanding comparing to other compression methods, the main advantage of our method is the concentration of distortions out of the medically most important areas. For this reason, from the medical point of view, our method guarantees high fidelity of reconstructed signal and, in consequence, high reliability of signal-derived diagnostic parameters. The other advantage is that the algorithm uses integer-represented values only, that simplifies the implementation in a clinical-use real-time recorder.

2006

Recently, the Multidimensional Multiscale Parser (MMP), an algorithm based on multiscale recurrent patterns, has been used to successfully compress data from ECG signals. Their quasi-periodic nature makes them natural candidates for the use of recurrent patterns. However, as many diagnostic relevant signals are far from periodic, the characteristics of MMP are not fully exploited. We have dealt with this problem by interpolating the ECG signal so that the interval between successive heart beats becomes constant. Following that, the algorithm subtracts the average period from the interpolated signal. Simulation results show that these modifications increase the rate-distortion performance.

Medical Engineering & Physics, 2001

This paper presents an ECG compressor based on optimized quantization of Discrete Cosine Transform (DCT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each DCT block is quantized using a quantization vector and a threshold vector that are specifically defined for each signal. These vectors are defined, via Lagrange multipliers, so that the estimated entropy is minimized for a given distortion in the reconstructed signal. The optimization method presented in this paper is an adaptation for ECG of a technique previously used for image compression. In the last step of the compressor here proposed, the quantized coefficients are coded by an arithmetic coder. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. To assess the performance of the proposed compressor, 2-minute sections of all 96 records of the MIT-BIH Arrhythmia Database were compressed at different PRD values, and the corresponding compression ratios were computed. We also present traces of test signals before and after the compression/decompression process. The results show that the proposed method achieves good compression ratios (CR) with excellent reconstruction quality. An average CR of 9.3:1 is achieved for PRD equal to 2.5%. Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors. 

Lecture Notes in Computer Science, 2007

The continuous demand for high performance and low cost electrocardiogram (ECG) processing systems have required the elaboration of more and more efficient and reliable ECG compression techniques. Such techniques face a tradeoff between compression ratio and retrieved quality, where the decrease of the last can compromise the subsequent use of the signal for clinical purposes. The objective of this work is to evaluate the validity and performance of an independent component analysis (ICA) based scheme used to efficiently compress ECG signals while introducing tests for a different type of record of the electrical activity of the heart, such as fetal magnetocardiogram (fMCG). As a result, the reconstructed signals underwent negligible visual deterioration, while achieving promising compression ratios.

2013

ECG (electrocardiogram) is a test that measures the electrical activity of the heart. The heart is a muscular organ that beats in rhythm to pump the blood through the body. Large amount of signal data needs to be stored and transmitted. So, it is necessary to compress the ECG signal data in an efficient way. In the past decades, many ECG compression methods have been proposed and these methods can be roughly classified into three categories: direct methods, parameter extraction methods and transform methods. In this paper a comparative study of Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete sine Transform (DST) and Discrete Cosine Transform-II (DCT-II). Records selected from MIT-BIH arrhythmia database are tested. For performance evaluation Compression Ratio (CR), Percent Root Mean Square differences (PRD) are used.

IEEE Transactions on Biomedical Engineering, 1993

A. Enis C;:: etin, Hayrettin Koyrnen, and M. Cengiz Aydm Abs/Tact-In this paper, a multilead ECG data compression method is presented, First, a linear transform is applied to the standard ECG lead signals which are highly correlated with each other. In this way a set of uncorrelated transform domain signals is obtained. Then, resulting transform domain signals are compressed using various coding meth ods, including multirate signal processing and transform domain coding techniques.