ECG Data Compression Research Articles

Compressive sensing of ECG signals using plug-and-play regularization

Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original signal from such compressed measurements remains a challenging problem. Traditional recovery methods are based on solving a regularized minimization problem, where a sparsity-promoting prior is used. In this paper, we propose an alternative iterative recovery algorithm based on the Plug-and-Play (PnP) method, which has recently become popular for imaging problems. In PnP, a powerful denoiser is used to implicitly perform regularization, instead of using hand-crafted regularizers; this has been found to be more successful than traditional methods. In this work, we use a PnP version of the Proximal Gradient Descent (PGD) algorithm for ECG recovery. To ensure mathematical convergence of the PnP algorithm, the signal denoiser in question needs to satisfy some technical conditions. We use a high-quality ECG signal denoiser fulfilling this condition by learning a Bayesian prior for small-sized signal patches. This guarantees that the proposed algorithm converges to a fixed point irrespective of the initialization. Importantly, through extensive experiments, we show that the reconstruction quality of the proposed method is superior to that of state-of-the-art methods.

ECG Data Compression Using Modified Run Length Encoding of Wavelet Coefficients for Holter Monitoring

ObjectiveIn cardiac patient-care, compression of long-term ECG data is essential to minimize the data storage requirement and transmission cost. Hence, this paper presents a novel electrocardiogram data compression technique which utilizes modified run-length encoding of wavelet coefficients. MethodFirst, wavelet transform is applied to the ECG data which decomposes it and packs maximum energy to less number of transform coefficients. The wavelet transform coefficients are quantized using dead-zone quantization. It discards small valued coefficients lying in the dead-zone interval while other coefficients are kept at the formulated quantized output interval. Among all the quantized coefficients, an average value is assigned to those coefficients for which energy packing efficiency is less than 99.99%. The obtained coefficients are encoded using modified run-length coding. It offers higher compression ratio than conventional run-length coding without any loss of information. ResultsCompression performance of the proposed technique is evaluated using different ECG records taken from the MIT-BIH arrhythmia database. The average compression performance in terms of compression ratio, percent root mean square difference, normalized percent mean square difference, and signal to noise ratio are 17.18, 3.92, 6.36, and 28.27 dB respectively for 48 ECG records. ConclusionThe compression results obtained by the proposed technique is better than techniques recently introduced by others. The proposed technique can be utilized for compression of ECG records of Holter monitoring.

A 2.63 μW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device.

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles. A modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 μW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate (P+) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio (CR) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

An enhanced lossless compression with cryptography hybrid mechanism for ECG biomedical signal monitoring

Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset.

A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

Compressed Sensing (CS) has been proposed as a low-complexity ECG data compression scheme for wearable wireless bio-sensor devices. However, CS decoding is characterized by high computational complexity. As a result, it represents a burden to the computational and energy resources of the network gateway node, where decoding is performed. In this article, we propose a Fast Compressive Electrocardiography (FCE) technique to address this problem. CS decoding in FCE is based on Weighted Regularized Least-Squares (WRLS), rather than the standard approach based on ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm minimization. The WRLS formulation takes into account prior knowledge of ECG signal properties to estimate an optimally compact and accurate representation of ECG signals. Numerical results show that decoding by FCE is on average 33 times faster than the fastest tested CS-based ECG decoding technique. In addition, high-quality ECG signal reconstruction by FCE is achieved at 32% higher compression ratio. Therefore, FCE can contribute to improving the overall energy and computational resource efficiency of CS-based remote ECG monitoring systems.

An Unsupervised feature extraction method based on self coded neural network

The way to tap the inherent law of ECG data and feature extraction is of vital in the absence of prior knowledge of the situation. This paper presents a self coded neural network model, which is reconstructed after compression of ECG data. The compression process eliminates the redundancy of data within the original data firstly from the low dimensional with more concise representations. In order to verify the features extracted from the neural network, we select the simplest and most direct distance based K nearest neighbour classification method. By using a self compiled neural network to extract features from the data set, the classification accuracy of K nearest neighbour classification can be greatly improved. The experimental results show that the unsupervised feature extraction method based on self coded neural network can effectively extract the features of the data in practical applications.

Analysis of lossless compression techniques time-frequency-based in ECG signal compression.

Technological advances introduce different methods for telecardiology. Telecardiology includes many applications and is one of the long-standing medical fields that have grown very well. In telecardiology, a very high amount of ECG data is recorded. Therefore, to compress electrocardiogram data (ECG), there is a need for an efficient technique and lossless compression. The compression of ECG data reduces the storage needs for a more efficient cardiological system and for analyzing and diagnosing the condition of the heart. In this paper, ECG signal data compression techniques are analyzed using the MIT-BIH database and then compared with the Apnea-ECG and Challenge 2017 Training bases. During the study, some of the various techniques of frequency analysis, range and time are widely used, such as run-time coding, AZTEC, Spin-Coding, FFT, DCT, DST, SAPA/FAN and DCT-II, where DCT and SAPA/FAN have the best compression performance compared to other methods.

WNC-ECGlet: Weighted non-convex minimization based reconstruction of compressively transmitted ECG using ECGlet

Abstract Data compression is very important in wireless body area network (WBAN), where a huge amount of ECG data is continuously transmitted to the receiver. Recent research has shown that compressive sensing (CS) is a better alternative for ECG data compression over traditional compression methods. In this work, we provide a method to improve CS-based ECG reconstruction that can be directly implemented in WBAN technology and hence, may enhance the use of WBAN technology in healthcare informatics and telemedicine. The methodology is named as WNC-ECGlet and consists of the following key contributions: (1) non-convex minimization is proposed for use in CS based ECG reconstruction, (2) weighted sparsity on wavelet coefficients of ECG signals is proposed to minimize for reconstruction, and (3) wavelet transform learning is proposed for ECG signals, named hereby, as ECGlet. An algorithm is provided to solve weighted non-convex minimization (WNC) problem. ECGlet is learned from an ensemble of ECG signals in the lifting framework rendering learning to be convex having closed-form solution with compactly supported filters that can be easily implemented on hardware. The learned wavelet transform is used as the sparsifying transform in the CS-based reconstruction of ECG signals transmitted compressively to the receiver. Extensive experiments are performed with comparing results of WNC-ECGlet with standard wavelets, l1 norm minimization and, Gaussian, Bernoulli and sparse binary sensing matrices on MIT-BIH Arrhythmia ECG dataset. The proposed WNC-ECGlet method is observed to perform better than conventional methods in CS-based ECG signal reconstruction.

Design of wavelet transform based electrocardiogram monitoring system

The new age advancements in information technology due to materials and integrated circuit (IC) technologies and their applications in biomedical sciences have made the healthcare facilities more compact and affordable for the aging population. Market trends in healthcare and related devices indicate a sharp rise in their demand. Hence the researchers have converged the efforts on designing more smart and advanced medical devices using IC technology. Among these devices, cardiac pacemakers have become a recurrent biomedical device which is engrafted in the human body to detect and monitor a person's heart beating rate. The data thus generated is processed for various medical usages and devices via wireless methods. Cardiovascular diseases (CVDs) or diseases related to the heart are due to abnormalities or disorders of the heart and blood vessels. Till date, limited literature is available which focuses on a single technique that can perform all of the ECG signal denoising, ECG detection, lossless data compression and wireless transmission. In this work, a joint approach for denoising, detection, compression, and wireless transmission of ECG signal is proposed. The modified biorthogonal wavelet transform is used for denoising, detection and lossless compression of ECG signal. To reduce the circuit complexity, biorthogonal wavelet transform is realized using linear phase structure. Further, it is found in this work that the usage of modified biorthogonal wavelet transform increases the detection accuracy and CR of the proposed design. Also, in this work, the Wi-Fi-based wireless protocol is used for compressed data transmission. The proposed ECG detector achieves the highest sensitivity and positive predictivity of 99.95% and 99.92%, respectively, with the MIT-BIH arrhythmia database. The use of modified biorthogonal 3.1 wavelet transform and run-length encoding (RLE) for the compression of ECG data achieves a higher compression ratio (CR) of 6.271. To justify the effectiveness of the proposed algorithm, which uses modified biorthogonal wavelet 3.1transform, the results are compared with the existing methods, namely, Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.

Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure

Heart rate monitoring and therapeutic devices include real-time sensing capabilities reflecting the state of the heart. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. It is observed that some detection techniques developed for ECG signal detection like artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results but their implementation on a silicon chip is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this paper, after a thorough analysis of various wavelet transforms, it is found that Biorthogonal wavelet transform is best suited to detect ECG signal's QRS complex. The main steps involved in ECG detection process consist of de-noising and locating different ECG peaks using adaptive slope prediction thresholding. Furthermore, the significant challenges involved in the wireless transmission of ECG data are data conversion and power consumption. As medical regulatory boards demand a lossless compression technique, lossless compression technique with a high bit compression ratio is highly required. Furthermore, in this work, LZMA based ECG data compression technique is proposed. The proposed methodology achieves the highest signal to noise ratio, and lowest root mean square error. Also, the proposed ECG detection technique is capable of distinguishing accurately between healthy, myocardial infarction, congestive heart failure and coronary artery disease patients with a detection accuracy, sensitivity, specificity, and error of 99.92%, 99.94%, 99.92% and 0.0013, respectively. The use of LZMA data compression of ECG data achieves a high compression ratio of 18.84. The advantages and effectiveness of the proposed algorithm are verified by comparing with the existing methods.

ECG data compression algorithm for tele-monitoring of cardiac patients

ECG data compression algorithm for tele-monitoring of cardiac patients

Image Compression, ECG Data Compression and Image Encryption by Binary Versions of Discrete Cosine Transform, Haar Transform and Discrete Hartley Transform

Image Compression, ECG Data Compression and Image Encryption by Binary Versions of Discrete Cosine Transform, Haar Transform and Discrete Hartley Transform

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.

A Review of ECG Data Compression Techniques

m (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. Keywordsm; ECG; Compression; CR; PRD; PRDN; QS

Compressed sensing-based method for electrocardiogram monitoring on wireless body sensor using binary matrix

Reducing the amount of wireless transmission data is beneficial for energy efficiency and lifetime of Wireless Body Sensor Networks WBSN. Compressed Sensing CS approach can be applied to Electrocardiogram ECG data compression with its low execution complexity in node end. However, sensor node is often resource-constrained which means reduced memory space for measurement matrix storage. This paper presents a compressed sensing based method for real-time multi-node ECG monitoring. A minimum 512 bytes binary random measurement matrix is designed which is very suitable for resource constrained WBSN sensor node. Result of R-point extraction indicates that heart beat rate can be precisely derived from recovered signal. Finally, real-time measurement based on compressed sensing is accomplished by using the ECG node we developed. Simulation and experiment show that the proposed method of compressed sensing with measurement matrix can fulfil the requirements of WBSN enabled ECG node both in reconstruction quality and reconstruction time.

A low complexity on-chip ECG data compression methodology targeting remote health-care applications.

In this paper, we propose a novel low complexity on-chip ECG data compression methodology targeting remote health-care applications. This is to the best of our knowledge the first attempt for on-chip reliable data compression. The proposed methodology has been implemented targeting Application Specific Integrated Circuit platform at 1 MHz at Vdd 1.62 V for UMC 130 nm technology library with 16 bits system word-length. Furthermore the proposed methodology results in a faithful reconstruction which has been validated using MIT-BIH PTB-DB as well as our institute's health repository IITH-DB. On an average about 90% compression is achieved with more than 83% R(2) statistics, 98% Cross Correlation and about 99% Regression between the original and the reconstructed data signifying the diagnostic accuracy. Subsequently the proposed methodology is capable of storing approximately 47 hrs of data in the same on-chip memory when compared to that of 5 hours of continuous data in the state of the art which would lead to enhanced diagnosis and prognosis in remote health-care.

Fitness Function Based ECG Data Compression Optimization with Genetic Algorithm – A Review

Fitness Function Based ECG Data Compression Optimization with Genetic Algorithm – A Review

EP-based wavelet coefficient quantization for linear distortion ECG data compression

Reconstruction quality maintenance is of the essence for ECG data compression due to the desire for diagnosis use. Quantization schemes with non-linear distortion characteristics usually result in time-consuming quality control that blocks real-time application. In this paper, a new wavelet coefficient quantization scheme based on an evolution program (EP) is proposed for wavelet-based ECG data compression. The EP search can create a stationary relationship among the quantization scales of multi-resolution levels. The stationary property implies that multi-level quantization scales can be controlled with a single variable. This hypothesis can lead to a simple design of linear distortion control with 3-D curve fitting technology. In addition, a competitive strategy is applied for alleviating data dependency effect. By using the ECG signals saved in MIT and PTB databases, many experiments were undertaken for the evaluation of compression performance, quality control efficiency, data dependency influence. The experimental results show that the new EP-based quantization scheme can obtain high compression performance and keep linear distortion behavior efficiency. This characteristic guarantees fast quality control even for the prediction model mismatching practical distortion curve.

A GA-based quantisation design scheme for ECG data compression with linear distortion control

Wavelet-based ECG data compression with multi-level adjustment mechanism is disadvantageous for reconstruction error control due to multi-toone mapping between multi-level quantisation scales and distortion. Linear distortion compression performance with easy error control is crucial for quality guarantee desired in diagnosis use. In this paper, a systematic approach is proposed for wavelet-based quantisation scheme design. This approach combines a Genetic Algorithm (GA) and Three-Dimensional (3-D) curvefitting technology to form a cascaded design process. The former is used to obtain high Compression Ratio (CR) and the latter aims to design a linear distortion control. Two kinds of ECG signals saved in MIT arrhythmia and ST change databases were used for the evaluation of compression performance and distortion control. The experimental results show that the design method can obtain linear distortion characteristic with high compression performance being kept.

ECG signal compression using ASCII character encoding and transmission via SMS

Software based efficient and reliable ECG data compression and transmission scheme is proposed here. The algorithm has been applied to various ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB). First of all, R-peaks are detected by differentiation and squaring technique and QRS regions are located. To achieve a strict lossless compression in the QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have used. The whole compression scheme is such that the compressed file contains only ASCII characters. These characters are transmitted using internet based Short Message Service (SMS) and at the receiving end, original ECG signal is brought back using just the reverse logic of compression. It is observed that the proposed algorithm can reduce the file size significantly (compression ratio: 22.47) preserving ECG signal morphology.