Electrocardiogram Compression Algorithm Research Articles

A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices

This brief presents a VLSI implementation of an efficient lossless compression scheme for electrocardiogram (ECG) data encoding to save storage space and reduce transmission time. As compression algorithm is able to save storage space and reduce transmission time, this opportunity has been seized by implementing memory-less design while working at a high clock speed in VLSI. ECG compression algorithm comprises two parts: an adaptive linear prediction technique and content-adaptive Golomb Rice code. An efficient and low power VLSI implementation of compression algorithm has been presented. To improve the performance, the proposed VLSI design uses bit shifting operations as a replacement for the different arithmetic operations. VLSI implementation has been applied to the MIT-BIH arrhythmia database which is able to achieve a lossless bit compression rate of 2.77. Moreover, VLSI architecture contains 3.1 K gate count and core of the chip consumes 27.2 nW of power while working at 1 KHz frequency. The core area is 0.05 mm2 in 90 nm CMOS process.

Telemedicine using ECG data compression

Electrocardiogram (ECG) is a useful tool for monitoring the health conditions of the subjects. ECG record contains a very important information about proper functioning of the heart and cardiovascular system as well. The ECG record is representation of deflections of physiological activities of the human heart during pumping of blood. This vital information is of absolutely useful to cardiologist for disease diagnosis. In case of ambulatory monitoring or critically cardiac disease it is required to store data for off-line analysis or transmission over public telephone lines. Such data generated is huge and needs to be stored by making optimum use of storage capabilities. For this reason we need effective data compression techniques. In the last four decades many ECG compression algorithm have been proposed by using different ECG compression techniques. One of the applications is to make use of these efficient compression schemes to provide low-cost and efficient medical healthcare. In the present paper, ECG data compression for long distance telemedicine is presented which promotes the utility of ECG data compression in the field of telemedicine i.e., tele-cardiology. The proposed telemedicine scheme is tested using ECG database with analysis and results.

A Remote Health Monitoring System for the Elderly Based on Smart Home Gateway.

This paper proposed a remote health monitoring system for the elderly based on smart home gateway. The proposed system consists of three parts: the smart clothing, the smart home gateway, and the health care server. The smart clothing collects the elderly's electrocardiogram (ECG) and motion signals. The home gateway is used for data transmission. The health care server provides services of data storage and user information management; it is constructed on the Windows-Apache-MySQL-PHP (WAMP) platform and is tested on the Ali Cloud platform. To resolve the issues of data overload and network congestion of the home gateway, an ECG compression algorithm is applied. System demonstration shows that the ECG signals and motion signals of the elderly can be monitored. Evaluation of the compression algorithm shows that it has a high compression ratio and low distortion and consumes little time, which is suitable for home gateways. The proposed system has good scalability, and it is simple to operate. It has the potential to provide long-term and continuous home health monitoring services for the elderly.

An ECG Acquisition System Prototype Design with Flexible PDMS Dry Electrodes and Variable Transform Length DCT-IV Based Compression Algorithm

In this paper, a system is designed using flexible poly-dimethylsiloxane (PDMS) dry electrodes instead of wet electrodes to acquire electrocardiogram (ECG) signals. This flexible PDMS dry electrode (FPDE) is revised from the hard commercial biopotential conductive snap to a flexible electrode using a replica method that provides a reliable attachment for the ECG measurement method. The measurement result shows the proposed FPDE, which has the ability to acquire ECG signals, is comparable with the traditional wet electrodes applied in medicine. In addition, an acquisition circuit design integrated with commercial ICs and an field-programmable gate array (FPGA) platform is built for the development of long-term ECG monitoring. A variable-transform-length DCT-IV-based ECG compression algorithm with a higher quality score (QS) and a better compressing ratio (CR) is further proposed to significantly reduce the large amount of recording data in both storage and transmission. The QS parameter, which denotes a ratio of a CR value to a percent rms difference (PRD) value, is another key index applied to fairly evaluate the performance of various compression algorithms. DCT-IV is used as a unified transform kernel for ECG signal encoding and decoding, because the forward DCT-IV formula is the same as its inverse. It can be easily converted into a compact hardware accelerator with fewer hardware resources. To fairly evaluate the proposed compression algorithm, ECG signals sourced from MIT-BIT arrhythmia database with a sampling rate of 360 Hz are employed as the test patterns. The simulation results show the averages of CR, PRD, and QS to be 6.86, 0.18, 2.60, 1.68, 32.19, and 39.86, respectively, for all 48 lead-II patterns of the MIT-BIH database. Compared with Lee $et al.$ ’s DCT-II based algorithm, the QS value of the proposed method exhibits a 76% improvement. The experimental results clearly show that the proposed system would be a better choice for achieving ECG signal acquisition in the future.

Compressed Sensing Based on the Characteristic Correlation of ECG in Hybrid Wireless Sensor Network

Hybrid wireless sensor network made up of wireless body area networks (WBANs) and cellular network provides support for telemedicine. In order to facilitate early diagnosis and treatment, WBANs collect and transmit crucial biomedical data to provide a continuous health monitoring by using various biomedical wireless sensors attached on or implanted in the human body. And then, collected signals are sent to a remote data center via cellular network. One of the features of WBAN is that its power consumption and sampling rate should be restricted to a minimum. Compressed sensing (CS) is an emerging signal acquisition/compression methodology which offers a prominent alternative to traditional signal acquisition. It has been proved that the successful recovery rate of multiple measurement vectors (MMV) model is higher than the single measurement vector (SMV) case. In this paper, we propose a simple algorithm of transforming the SMV model into MMV model based on the correlation of electrocardiogram (ECG), such that the MMV model can be used for general ECG signals rather than only several special signals. Experimental results show that its recovery quality is better than some existing CS-based ECG compression algorithms and sufficient for practical use.

Wavelet transform and Huffman coding based electrocardiogram compression algorithm: Application to telecardiology

We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.

PQRST파 특징 기반 신호의 분류를 이용한 심전도 압축 알고리즘 성능 평가

심전도의 압축은 시스템의 처리 속도를 높일 뿐만 아니라 신호의 전송량, 장기적인 기록 데이터 저장량을 줄일 수 있다. 본 논문에서는 기존의 심전도 데이터의 손실 혹은 무 손실 압축 알고리즘에 대한 성능 평가가 엔지니어의 관점에서 PRD(Percent RMS Difference)와 CR(Compression Ratio)을 측정하였다면 심전도를 진단하는 진단자의 관점에서 압축의 성능 평가에 대한 연구를 하였다. 일반적으로 심전도 데이터의 압축이 진단에 영향을 미치지 않게 하기위해서는 압축 후 복원된 PQRST파의 위치, 길이, 진폭, 파의 형태 등 진단에 필요한 것들이 손상되어선 안 된다. 대표적인 심전도 압축 알고리즘 AZTEC은 기존의 성능평가에 그 효율성이 검증되었지만 진단자의 관점에서 새로운 성능평가를 제시한다. An ECG(Electrocardiogram) compression can increase the processing speed of system as well as reduce amount of signal transmission and data storage of long-term records. Whereas conventional performance evaluations of loss or lossless compression algorithms measure PRD(Percent RMS Difference) and CR(Compression Ratio) in the viewpoint of engineers, this paper focused on the performance evaluations of compression algorithms in the viewpoint of diagnostician who diagnosis ECG. Generally, for not effecting the diagnosis in the ECG compression, the position, length, amplitude and waveform of the restored signal of PQRST wave should not be damaged. AZTEC, a typical ECG compression algorithm, is validated its effectiveness in conventional performance evaluation. In this paper, we propose novel performance evaluation of AZTEC in the viewpoint of diagnostician.

ECG compression using uniform scalar dead-zone quantization and conditional entropy coding

A new wavelet-based method for the compression of electrocardiogram (ECG) data is presented. A discrete wavelet transform (DWT) is applied to the digitized ECG signal. The DWT coefficients are first quantized with a uniform scalar dead-zone quantizer, and then the quantized coefficients are decomposed into four symbol streams, representing a binary significance stream, the signs, the positions of the most significant bits, and the residual bits. An adaptive arithmetic coder with several different context models is employed for the entropy coding of these symbol streams. Simulation results on several records from the MIT-BIH arrhythmia database show that the proposed coding algorithm outperforms some recently developed ECG compression algorithms.

A 2-D ECG compression algorithm based on wavelet transform and vector quantization

In this paper, we proposed a new two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression algorithm. A 1-D ECG data is first segmented and aligned to a 2-D data array, thus the two kinds of correlation of heartbeat signals can be fully utilized. And then 2-D wavelet transform is applied to the constructed 2-D ECG data array. A modified vector quantization (VQ) is employed to the wavelet coefficients. This modified VQ algorithm constructs a new tree vector (TV) which well utilizing the characteristics of the wavelet coefficients. Records selected from the MIT/BIH arrhythmia database are tested contrastively using the proposed algorithm, some compression algorithms based on wavelet transform and the other 2-D ECG compression algorithms. The experimental results show that the proposed method is suitable for various morphologies of ECG data, and that it achieves higher compression ratio with the characteristic features well preserved.

Wavelet-Based Low-Delay ECG Compression Algorithm for Continuous ECG Transmission

The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noise.

A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients

This paper describes a new algorithm for electrocardiogram (ECG) compression. The main goal of the algorithm is to reduce the bit rate while keeping the reconstructed signal distortion at a clinically acceptable level. It is based on the compression of the linearly predicted residuals of the wavelet coefficients of the signal. In this algorithm, the input signal is divided into blocks and each block goes through a discrete wavelet transform; then the resulting wavelet coefficients are linearly predicted. In this way, a set of uncorrelated transform domain signals is obtained. These signals are compressed using various coding methods, including modified run-length and Huffman coding techniques. The error corresponding to the difference between the wavelet coefficients and the predicted coefficients is minimized in order to get the best predictor. The method is assessed through the use of percent root-mean square difference (PRD) and visual inspection measures. By this compression method, small PRD and high compression ratio with low implementation complexity are achieved. Finally, we have compared the performance of the ECG compression algorithm on data from the MIT-BIH database.

ECG data compression using optimal non-orthogonal wavelet transform

This paper introduces an effective technique for the compression of electrocardiogram (ECG) signals. The technique is based on a new class of non-orthogonal discrete wavelet transform (DWT). The performance of ECG compression algorithm is measured by its ability to minimize distortion while retaining all clinically significant features of the signal. The percent root–mean square difference (PRD) is used as an accepted standard for measuring the signal distortion. However, there is no standard for measuring the clinically significant features retained after signal reconstruction. The coefficients of the DWT are calculated such that the square of the difference between the original signal and the reconstructed one is minimum in least mean square sense. The resulting transforms deal with signals of arbitrary lengths; that means the signal length is not restricted to be a multiple of power of 2. Numerical results comparing the performance of the constructed non-orthogonal transform with that of W-transform and Daubechies D 4 orthogonal transform are given. These results show that, independent of signal length, the decomposition of the signal up to the fourth level is sufficient for getting minimum PRD. In addition, the proposed technique yields the lowest PRD compared to the other two algorithms and for a compression ratio less than 10 the optimal transform can be obtained for only one ECG period. However, for a higher compression ratio the PRD is smaller for long signals.

A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals.

In this paper, a novel family of compression algorithms is presented, which is designed to exploit the redundancy of one-dimensional (1-D) semiperiodical biomedical signals resulting from the cyclic nature of the underlying physical process. The basic idea is that a pool of past-seen cycles is maintained and cycles to be encoded can be stored as transformed versions of those residing in the pool. Conceptually, this approach is an extension of dictionary-based coding schemes used for text compression to signal patterns residing in an n-dimensional space. A cycle transformation method is introduced in order to render the pattern matching process practical and to enable cycle substitution. Based on the principles of the algorithmic family and this transformation method, an electrocardiogram (ECG)-oriented algorithm is implemented and thoroughly tested. The performance of this implementation is examined theoretically and deductions about the optimal algorithm settings are made. The ECG compression algorithm is superior to the average beat subtraction algorithm as proposed by Hamilton and Tompkins in cases where high compression ratios are required.

An adaptive real-time ECG compression algorithm with variable threshold.

A real-time compression algorithm has been developed which is suitable for both real-time ECG (electrocardiogram) transmission and ECG data storing. The algorithm represents a modification of the amplitude zone time epoch coding (AZTEC) technique extended with several statistical parameters used to calculate the variable threshold. The proposed algorithm has been applied in the design of a pacemaker followup system for the online ECG data transmission from the pacemaker implanted in a human being to the computer system located at the clinic. >