ECG Data Compression Algorithm Research Articles

ECG data compression algorithm for tele-monitoring of cardiac patients

ECG data compression algorithm for tele-monitoring of cardiac patients

Application of Discrete Wavelet Transform with Changing Presentation of Coefficients in Data Compression in Mobile ECG Monitoring Systems

The article studies the problem of compression of data recorded by ECG sensors of mobile heart activity monitoring systems. An ECG data compression algorithm is presented which is based on transformation of coefficients of a discrete wavelet transform implemented using a computing device with limited processing power and low power consumption. The algorithm was tested on real signals and demonstrated its effectiveness.

An ECG signal compression technique using ASCII character encoding

Efficient software based new ECG data compression algorithm is developed here. The whole algorithm is written in the C-platform. The algorithm has been applied to various ECG data of all the 12 leads (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6), taken from PTB Diagnostic ECG Database (ptbdb). In this method, only the “Voltage” values are gathered from the corresponding input ECG data and then those are multiplied by a considerably large integer number to convert them into integer. In the next step, these integer numbers are grouped maintaining some logical criteria. This grouping is functioning as the main compression technique. In this technique the grouping has been done in such a way that every grouped number resides under valid ASCII value. 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. Basically, ECG is a voltage–time characteristic and since the sampling time is constant, the “time” data can be automatically reconstructed at the receiving end. Therefore concentration has been given only to compress the ‘Voltage’ values.

A Real-Time ECG Data Compression and Transmission Algorithm for an e-Health Device

This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

The Research of ECG Diagnosis System Based on Support Vector Machines

The QRS complex is the most striking waveform within the ECG. Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Identify the QRS complex wave, we can further identify the P wave, T wave, etc. After the noise in ECG is Butterworth filtered, we present an algorithm using Matlab software to detect QRS complex, P wave and T wave, then analysis the ECG signal based on wavelet transform to detect the parameters. Finally，according to these parameters, we diagnosed atrial premature beats, ventricular tachycardia, supraventricular tachycardia and normal ECG based on support vector machines.

AZTDIS — a two-phase real-time ECG data compressor

An ECG sampled at a rate of 360 samples s −1 or more produces a large amount of redundant data that are difficult to store and transmit; we therefore need a process to represent the signals with clinically acceptable fidelity and with as small a number of code bits as possible. In this paper, a real-time ECG data-compression algorithm, AZTDIS, is presented. AZTDIS is an efficient algorithm which locates significant samples and at the same time encodes linear segments between them by using linear interpolation. The significant samples selected include, but are not limited to, the samples that have significant displacement from the encoded signal such that the allowed maximal error is limited to a constant ϵ, which is specified by the user. The way that AZTDIS computes the displacement of a sample from the encoded signal guarantees that the high activity regions are more accurately coded. The results from AZTDIS are compared with those from the well-known data-compression algorithm, AZTEC, which is also a real-time algorithm. It is found that under the same bit rate, a considerable improvement of root-mean-square error (RMS err) can be achieved by employing the proposed AZTDIS algorithm. An average value of RMS err of 9.715 can be achieved even at an average bit rate of 0.543 bits per sample by employing AZTDIS. By tuning the allowed maximal error of AZTDIS such that it has similar bit rate to AZTEC, the average value of RMS err achieved by AZTDIS is 5.554 while the average value of RMS err achieved by AZTEC under the same bit rate is 19.368.

Fast ECG data compression algorithms suitable for microprocessor systems

ECG data compression techniques have received extensive attention in ECG analysis. Numerous data compression algorithms for ECG signals have been proposed during the last three decades. We describe two algorithms based on the scan-along polygonal approximation algorithm (SAPA) that are suitable for multichannel ECG data reduction on a microprocessor-based system. One represents a modification of SAPA (MSAPA) which adopts the method of integer division table searching to speed up data reduction; the other (CSAPA) combines MSAPA and TP, a turning-point algorithm, to preserve ST segment signals. Results show that our algorithms achieve a compression ratio of more than 5:1 and a percent rms difference (PRD) to the original signal of less than 3.5%. In addition, the maximum execution time of MSAPA for processing one data point is about 50μ s. Moreover, the CSAPA algorithm retains all of the details of the ST segment, which are important in ischaemia diagnosis, by employing the TP algorithm.

Theoretical and experimental rate distortion performance in compression of ambulatory ECGs

We compare ECG data compression algorithms based on signal entropy for a given mean-square-error (MSE) compression distortion. By defining the distortion in terms of the MSE and assuming the ECG signal to be a Gaussian process we are able to estimate theoretical rate distortion bounds from average ECG power spectra. These rate distortion bounds give estimates of the minimum bits per second (bps) required for storage of ECG data with a given MSE regardless of compression method. From average power spectra of the MIT/BIH arrhythmia database we have estimated rate distortion bounds for ambulatory ECG data, both before and after average beat subtraction. These rate distortion estimates indicate that, regardless of distortion, average beat subtraction reduces the theoretical minimum data rate required for ECG storage by approximately 100 bits per second (bps). Our estimates also indicate that practical ambulatory recording requires a compression distortion on the order of 11 microV rms. We have compared the performance of common ECG compression algorithms on data from the MIT/BIH database. We sampled and quantized the data to give distortion levels of 2, 5, 8, 11, and 14 microV rms. These results indicate that, when sample rates and quantization levels are chosen for optimal rate distortion performance, minimum data rates can be achieved by average beat subtraction followed by first differencing of the residual signal. Achievable data rates approximate our theoretical estimates at low distortion levels and are within 60 bps at higher distortion levels.

Microcomputer-based coronary care unit central station.

A four-bed central station that can be connected to any commercial intensive-care bedside monitor was developed. The system is based on a personal computer (IBM-AT compatible) as a local unit and on a microcontroller Intel 8031 as a remote unit. Four ECG signals are low-pass filtered, multiplexed, sampled at 256-Hz per channel, 8-bit A/D converted, preprocessed, and converted to a serial format RS-232 by the remote unit. The real-time display of the signals is at the standard speed of 25 and 50 mm/sec. Heartrate, alarms, trend plots, and general patient data are shown on an Olivetti M280 and EGA 13'' color monitor as the local unit. The communication speed was set at 57.6 Kbaud full duplex. Additionally, to reach standard monitoring sweep rates using a 13'' screen with 640 x 350 pixels, an ECG data-compression algorithm was implemented in the remote unit. This unit can support up to eight input channels and can work with any personal computer, via RS-232, with the appropriate software. It also allows other signal preprocessing software that could be developed, such as QRS detection or ST segment quantification, to be loaded into its random access memory and to be run under PC command. The development of this system demonstrated the use of a widespread piece of commercial equipment, the PC, in a very specific application, CCU monitoring, assuring low-cost system implementation. This feature is particularly attractive in upgrading existing CCU units in less developed countries.