ECG data compression techniques-a unified approach

Over the years, the employment of computers in the field of patient monitoring has gained grounds. The rational explanation being, the computers primarily facilitate health care officials to cope with vast amount of data, like records of ECG waveforms of numerous patients. However, enormous data would require enormous storage space eventually causing the system to crash. Hence, a need is felt to reduce the storage requirements of data, in this case ECG samples. This is achieved by employing the data compression techniques.

Information and Knowledge Management, 2012

Biological signal compression and especially ECG has an important role in the diagnosis, prognosis and survival analysis of heart diseases. Various techniques have been proposed over the years addressing the signal compression. Compression of digital electrocardiogram (ECG) signals is desirable for three reasons-economic use of storage data, reduction of the data transmission rate and transmission bandwidth conversation. ECG signal. In this paper a comparative study of Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Discrete Cosine compression is used for telemedicine field and re Transform (DCT) and Wavelet Transform (WT) transform based approach is carried out. Different ECG signals are tested from MIT-BIH arrhythmia database using MATLAB software. The experimental results are obtained for Percent Root Mean Square Difference (PRD), Signal to Noise ratio (SNR) and Compression ratio (CR). The result of ECG signal compression shows better compression performance in DWT compared to DFT, FFT and DCT.

Communications in Computer and Information Science, 2011

In this paper, a transform based methodology is presented for compression of electrocardiogram (ECG) signal. The methodology employs different transforms such as Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT). A comparative study of performance of different transforms for ECG signal is made in terms of Compression ratio (CR), Percent root mean square difference (PRD), Mean square error (MSE), Maximum error (ME) and Signal-to-noise ratio (SNR). The simulation results included illustrate the effectiveness of these transforms in biomedical signal processing. When compared, Discrete Cosine Transform and Fast Fourier Transform give better compression ratio, while Discrete Wavelet Transform yields good fidelity parameters with comparable compression ratio.

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.

2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), 2011

Efficient and reliable electrocardiogram (ECG) compression system can increase the processing speed of realtime ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In the present paper, a software based effective ECG data compression algorithm is proposed. The whole algorithm is written in C-platform. The algorithm is tested on various ECG data of all the 12 leads taken from PTB Diagnostic ECG Database (PTB-DB). In this compression methodology, all the R-Peaks are detected at first by differentiation technique and QRS regions are located. To achieve a strict lossless compression in QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have developed. In lossless compression method a difference array has been generated from the corresponding input ECG "Voltage" values and then those are multiplied by a considerably large integer number to convert them into integer. In the next step, theses integer numbers are grouped in both forward and reverse direction maintaining some logical criteria. Then all the grouped numbers along with sign bit and other necessary information (position of critical numbers, forward/reverse grouping etc.) are converted into their corresponding ASCII characters. Whereas in lossy area, first of all, the sampling frequency of the original ECG signal is reduced to one half and then, only the "Voltage" values are gathered from the corresponding input ECG data and those are amplified and grouped only in forward direction. Then all the grouped numbers along with sign bit and other necessary information are converted into their corresponding ASCII characters. It is observed that this proposed algorithm can reduce the file size significantly. The data reconstruction algorithm has also been developed using the reversed logic and it is seen that data is reconstructed preserving the significant ECG signal morphology.

IRJET, 2020

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 ϲonseϲutive 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 ϲonϲludes with the presentation of a framework for evaluation and comparison of ECG compression schemes..

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.

Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. This paper examines lossless data compression algorithm for ECG data by using new method to process the ECG image strip, and compares their performance. We confirming that the proposed strategy exhibits competitive performances compared with the most popular compressors used for ECG compression.

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.

2017 Computing in Cardiology (CinC), 2017

ECG of a person is being recorded for diagnosis of heart diseases, regular checkups, fitness and many other diseases also. Hence, huge amount of ECG data is being generated daily in hospitals. ECG recording and monitoring consume lots of memory space of digital computers. Data compression plays a vital role in reducing storage space and utilizing transmission bandwidth effectively. Objective of the research work is to propose a robust and effective method for ECG signal compression. This paper includes extraction of key morphological features, statistical features from decomposed signal, analysis in wavelet domain and classification of feature set. To reduce dimensionality of feature set, principle component analysis (PCA) is applied. Accuracy achieved with 15 principle components is same as pure wavelet transform with significant improvement in compression ratio (CR) by a factor of 4:1. MITBIH Arrhythmia database is used for experimentation. Accuracy obtained with K nearest neighbo...

Cardiac diseases constitute the main cause of mortality around the globe. For detection and identification of cardiac problems, it is very important to monitor the patient's heart activities for long periods during his normal daily life. The recorded signal that contains information about the condition of the heart called electrocardiogram (ECG). As a result, long recording of ECG signal amounts to huge data sizes. In this work, a robust lossless ECG data compression scheme for real-time applications is proposed. The developed algorithm has the advantages of lossy compression without introducing any distortion to the reconstructed signal. The ECG signals under test were taken from the PTB Diagnostic ECG Database. The compression procedure is simple and provides a high compression ratio compared to other lossless ECG compression methods. The compressed ECG data is generated as a text file. The decompression scheme has also been developed using the reverse logic and it is observed that there is no difference between original and reconstructed ECG signal.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001

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.

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.

Storage and transmission limitations have made electrocardiogram (ECG) data compression an important aspect for ECG computerized systems. In this paper a lossless method based on modified American standard code for information Interchange (ASCII) character encoding for ECG data compression have been proposed. The Proposed method consists of compression algorithm comprising sign count; generation of array representing ECG sample's signs (+ve,-ve alternatively), adaptive amplification factor; and grouping method of ECG samples and a reverse process for ECG reconstruction. The ability of the proposed compression algorithm has been investigated on the MIT-BIH Arrhythmia Database. The average percentage root mean square difference (PRD) of about 0.32, compression ratio (CR) of about 8.38, quality score (QS) of about 26.02, percent root mean square difference normalized (PRDN) of about 0.56, root mean square error (RMS) of about 0.0018 and SNR of about 45.46 was achieved on MIT-BIH data. The method is also compared with other compression algorithms and showed superior performance in term of PRD, CR, QS, PRDN, RMS and SNR. The novelty of proposed method is the nearly exact reproduction of the original signal (PRD=0.32) and a moderate CR.