ECG Signal Compression for Diverse Transforms

Journal of Medical Engineering & Technology, 2009

A new hybrid two-stage electrocardiogram (ECG) signal compression method based on the modified discrete cosine transform (MDCT) and discrete wavelet transform (DWT) is proposed. The ECG signal is partitioned into blocks and the MDCT is applied to each block to decorrelate the spectral information. Then, the DWT is applied to the resulting MDCT coefficients. Removing spectral redundancy is achieved by compressing the subordinate components more than the dominant components. The resulting wavelet coefficients are then thresholded and compressed using energy packing and binary-significant map coding technique for storage space saving. Experiments on ECG records from the MIT-BIH database are performed with various combinations of MDCT and wavelet filters at different transformation levels, and quantization intervals. The decompressed signals are evaluated using percentage rms error (PRD) and zero-mean rms error (PRD(1)) measures. The results showed that the proposed method provides low ...

International Journal of Advanced Computer Science and Applications, 2016

The storage capacity of the ECG records presents an important issue in the medical practices. These data could contain hours of recording, which needs a large space for storage to save these records. The compression of the ECG signal is widely used to deal with this issue. The problem with this process is the possibility of losing some important features of the ECG signal. This loss could influence negatively the analyzing of the heart condition. In this paper, we shall propose an efficient method of the ECG signal compression using the discrete wavelet transform and the run length encoding. This method is based on the decomposition of the ECG signal, the thresholding stage and the encoding of the final data. This method is tested on some of the MIT-BIH arrhythmia signals from the international database Physionet. This method shows high performances comparing to other methods recently published.

The storage capacity of the ECG records presents an important issue in the medical practices. These data could contain hours of recording, which needs a large space for storage to save these records. The compression of the ECG signal is widely used to deal with this issue. The problem with this process is the possibility of losing some important features of the ECG signal. This loss could influence negatively the analyzing of the heart condition. In this paper, we shall propose an efficient method of the ECG signal compression using the discrete wavelet transform and the run length encoding. This method is based on the decomposition of the ECG signal, the thresholding stage and the encoding of the final data. This method is tested on some of the MIT-BIH arrhythmia signals from the international database Physionet. This method shows high performances comparing to other methods recently published.

International journal of engineering research and technology, 2013

Electrocardiogram (ECG) is a graphic tracing of the variations in electrical potential caused by the excitation of the heart muscle and detected at the body surface. ECG plays a significant role in diagnosing most of the cardiac diseases. Many types of ECG recordings generate a vast amount of data. ECG compression becomes mandatory to efficiently store and retrieve this data from medical database. ECG data compression technique is needed to reduce the amount of data to be transmitted, stored and analyzed, without losing the clinical information content. Thus, the need for effective ECG compression techniques is of great importance. Many existing compression algorithms have shown some success in electrocardiogram compression; however, algorithms that produce better compression ratios and of minimum PRD data in the reconstructed signal are needed. In the present report, we discuss the survey of work done by various researchers. 1.

Electrocardiogram (ECG) compression transformation techniques play a vital role in diagnosing various cardiac patients where traditional method fails to detect problem in their Heart’s working. Each cardiac ECG cycle represents various amplitude and intervals under P-QRS-T waves. This compression scheme evaluates amplitude and intervals in ECG signal for analysis. Each P-QRS-T wave represents the electrical activity of the patient. Recently a vast research and techniques have been developed for analyzing the various aspects of ECG signal. Introduced scheme were mostly based upon the Wavelet transformation, Cosine transformation, Fourier transformation and other techniques. All these techniques and algorithms have their pros and cons. Introduced paper enlightens the various techniques and transformation introduced previously for compression of ECG signal. Summation to above this paper also includes a comparative study of various techniques used by researchers for compression of ECG signal.

2013

This research paper presents an Electrocardiography (ECG) compression based on optimized quantization of Fast Fourier Transform (FFT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each FFT block is quantized using a quantization vector that are specifically defined for each signal. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. 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). The objective is realized by constructing and designing an algorithm & software for compression which would enable optimal storage of ECG signals in the database for future references.

International Journal of Computer Applications, 2015

Electrocardiogram (ECG) data compression reduced the storage requirements to develop a more efficient tele-cardiology system for cardiac analysis and diagnosis. The ECG compression without loss of diagnostic information is based on the fact that consecutive samples of the digitized ECG carry redundant information that can be removed with very less computing effort. This paper focuses on providing a comparison of the major techniques (direct, transform, parameter extraction and 2D approaches) of ECG data compression which are intended to attain a lossless compressed data with relatively high compression ratio (CR) and low percent root mean square difference (PRD). The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

In medical practices, the storage space of electrocardiogram (ECG) records is a major concern. These records can contain hours of recording, necessitating a large amount of storage space. This problem is commonly addressed by compressing the ECG signal. The proposed work deal with the ECG signal compression method for ECG signals using discrete wavelet transform (DWT). The DWT appeared as powerful tools to compact signals and shows a signal in another time-frequency representation. It is very appropriate in the elimination & removal of redundancy. The ECG signals are decomposed using DWT. After that, the coefficients that result from DWT are threshold depending on the energy packing efficiency (EPE) of the signal. The compression is achieved by the quantization and dual encoding techniques (run-length encoding & Huffman encoding). The dual encoding technique compresses data significantly. The result of the proposed method shows better performance with compression ratios and good quality reconstructed signals. For example, the compression ratio (CR)=20.6, 10.7 and 11.1 with percent root mean square difference (PRD)=1%, 0.9% and 1% for using different DWT (Haar, db2 and FK4) Respectively.

International Journal of Computer Applications, 2017

Electrocardiogram (ECG) is the technique that is used to record the electrical signal of the heart over a time interval by using the electrodes, positioned on a patient's body. The signals collected from the body needs to be processed and compressed before directing to monitoring center. Electrocardiogram (ECG) data compressions minimize the necessities of storage to generate a more proficient telecardiology system for the cardiac exploration and diagnosis. This paper focus on the evaluation of several compression schemes for ECG data compression and also provides the comparison of the various ECG compression techniques such as Turning Point, Delta Coding, AZTEC, CORTES, DCT etc. in terms of different performance metrics like Compression Ratio (CR), Percent Mean Square Difference (PRD) and Quality Score (QS).

2013

Jalandhar-144011 Abstract— Electrocardiogram (ECG) plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. Many types of ECG recordings generate a vast amount of data. ECG compression becomes mandatory to efficiently store and retrieve this data from medical database. Recently, numerous research and techniques have been developed for compression of the signal. These techniques are essential to a variety of application ranging from diagnostic to ambulatory ECG's. Thus, the need for effective ECG compression techniques is of great importance. Many existing compression algorithms have shown some success in electrocardiogram compression; however, algorithms that produce better compression ratios and less loss of data in the reconstructed signal are needed. This proposed paper discusses various techniques proposed earlier in literature for compression of an ECG signal and provide comparative study of these...