Query: Compression of ECG signals

title: An efficient compression of ECG signals using deep convolutional autoencoders
abstract: Advances in information technology have facilitated the retrieval and processing of biomedical data. Especially with wearable technologies and mobile platforms, we are able to follow our healthcare data, such as electrocardiograms (ECG) in real time. However, the hardware resources of these technologies are limited. For this reason, the optimal storage and safe transmission of the personal health data is critical. This study proposes a new deep convolutional autoencoder (CAE) model for compressing ECG signals.  In this paper, a deep network structure of 27 layers consisting of encoder and decoder parts is designed. In the encoder section of this model, the signals are reduced to low dimensional vectors; and in the decoder section, the signals are reconstructed. The deep learning approach provides the representations of the low and high levels of signals in the hidden layers of the model. Hence, the original signal can be reconstructed with minimal loss. Very different from traditional linear transformation methods, a deep compression approach implies that it can learn to use different ECG records automatically.  The performance was evaluated on an experimental data set comprising 4800 ECG fragments from 48 unique clinical patients. The compression rate (CR) of the proposed model was 32.25, and the average PRD value was 2.73% These favourable observation suggest that our deep model can allow secure data transfer in a low dimensional form to remote medical centers. We present an effective compression approach that can potentially be used in wearable devices, e health applications, telemetry and Holter systems.

title: Effective high compression of ECG signals at low level distortion
abstract: An effective method for compression of ECG signals, which falls within the transform lossy compression category, is proposed. The transformation is realized by a fast wavelet transform. The effectiveness of the approach, in relation to the simplicity and speed of its implementation, is a consequence of the efficient storage of the outputs of the algorithm which is realized in compressed Hierarchical Data Format. The compression performance is tested on the MIT BIH Arrhythmia database producing compression results which largely improve upon recently reported benchmarks on the same database. For a distortion corresponding to a percentage root mean square difference (PRD) of 0.53, in mean value, the achieved average compression ratio is 23.17 with quality score of 43.93. For a mean value of PRD up to 1.71 the compression ratio increases up to 62.5. The compression of a 30 min record is realized in an average time of 0.14 s. The insignificant delay for the compression process, together with the high compression ratio achieved at low level distortion and the negligible time for the signal recovery, uphold the suitability of the technique for supporting distant clinical health care.

title: An Application of Image Processing Technique for Compression of ECG Signals Based on Region of Interest Strategy
abstract: In this paper, a novel Region of Interest (ROI) based 2 Dimensional (2D) compression of ECG signals using JPEG2000 compression standard is proposed. Because of its high efficiency JPEG2000 is the global benchmark for compression of stationary images. This work is to illustrate that the JPEG2000 compression technique is not only restricted to compress images but also it can be applied to compress ECG signals. First the one dimensional ECG signal is transformed to 2D representation or image to explore the correlation among the samples and among the beats. This necessitates few steps that includes QRS detection and arrangement of QRS complex at relative position, Period sorting, mean extension, period normalization, amplitude normalization and locating ROI. Then this resultant 2D ECG data array is compressed using JPEG2000. The ROI region is extracted by applying the Otsu thresholding and the vertical projection profile on the resultant 2D ECG data. The core idea in this work is to compress the ROI region at low compression rate and non ROI region at high compression rate. The proposed method is evaluated on the selected data from MITs Beth Israel hospital and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher Compression Ratio (CR) and moderate Percentage root mean square difference (PRD)

title: An Image Processing Approach for Compression of ECG Signals Based on 2D RLE and SPIHT
abstract: This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter beat and intra beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut and align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE) and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage root mean square difference (PRD)

title: VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals
abstract: The measurement of electrical activity of the heart via electrodes is named as Electrocardiography (ECG) An efficient compression technique using the compressive sensing method is required. Compressive Sensing (CS) holds the promise to be a key for acquisition and reconstruction of sparse signals. The reconstruction of such signals makes sampling rates below Nyquist rate. In this work, a novel framework was proposed that is based on the idea of CS theory for the compression of mother and fetal heart beats. The proposed scheme is based on the sparse representation of the components derived from the curvelet transform of the original Electrocardiogram (ECG) signal. The ECG signals may be approximated by a few coefficients that can be taken from a wavelet basis. This fact allows a compressed sensing approach for ECG signal compression to be introduced and to be a domain of search. ECG signals illustrate redundancy between adjacent heart beats. This redundancy implies a high fraction of common support between consecutive heart beats. The main contribution of this paper lies in the using of curvelet transform in order to generate sparsity in ECG signal. This transformation is considered an excellent approach as illustrated in this paper. Simulation results represent a better approach than Discrete Wavelet Transform (DWT) that is based on compression of ECG. MIT BIH database is used for experimentation. The MIT BIH database contains different kinds of ECG signals that include both abnormal ECG and normal ECG, which have different sampling rates. MATLAB tool is used for simulation purpose. The novelty of the method is that the Compression Ratio (CR) achieved by detail coefficients is better. The performance measure of the reconstructed signal is carried out by Percentage Root Mean Difference (PRD) This paper also introduces the efficient realization of the different transformation techniques using FPGA. Thus the contribution of this paper lies into two main parts. The first part is specialized in determining the proper transformation that is used in the compression of ECG signals. The second part of the contribution is summarized in using suitable hardware to implement this design. Architecture can be based on the ideas of parallelism and pipelining to get the minimum throughput and speed. Architecture is cascade and simple for calculating curvelet coefficients. The reduction of the memory size can be done by splitting ROM table. The description and functionalities of the design are modeled by Verilog HDL. The simulation and synthesis methodology are used on Virtex II Pro FPGA that uses less number of resources of the FPGA. General Terms Discrete Wavelet Transform; Performance Measure;

title: Study of various data compression techniques used in lossless compression of ECG signals
abstract: As we know that developments in technology are introducing various methods for Tele cardiology. Tele cardiology includes many of the applications and this is one of the fields in telemedicine which have seen excellent growth. In the procedures of Tele cardiology we record a extremely large amount of ECG real time data. Therefore we require an efficient and lossless technique that is able to perform compression of recorded ECG signals. In this paper we have studied and analyzed various lossless data compression techniques used in the compression of ECG signals. In the course of studying various techniques we have presented the analysis of some most widely used time domain techniques those are AZTEC (Amplitude zone time epoch coding) technique and Turning point technique (TP) and in transformation based compression techniques we have presented the study of Discrete Cosine Transform technique (DCT) performed with Huffman coding technique and Empirical Mode Decomposition (EMD) technique. The overall performance of all these techniques are studied and analyzed on the basis of two main parameters those are the compression ratio (CR) and Percent Root means square Difference (PRD) We have used the data base of physionet.org website for the calculation of CR and PRD. We have calculated and compared the CR and PRD values using all above discussed techniques for 28 sets of the recorded data.

title: Two Dimensional Compression of ECG Signals Using HEVC Intra Encoder and Pre processing Techniques
abstract: The paper presents the proposal of an electrocardiogram (ECG) compressor based on two dimensional coding. The signal is pre processed and the R waves detected, from these waves the signal is segmented and arranged in an N x M matrix, composed of M segments and N the maximum length between two R waves. Once this matrix is composed, the interpolation process is performed so that all segments M have the same size N. With the interpolated matrix a pre processing is applied to improve the two dimensional correlation of the matrix. This is done by rearranging the segments so that the correlation of the segments be maximized. Some widely adopted image compression algorithm is used to reduce the volume of data. In the present work, the intra mode of the High Efficiency Video Coding (HEVC) is applied to encode real ECG signals obtained through the MIT BIH Arrhythmia Database. These signals impose a greater challenge for the proposed system. The application of this proposal demonstrates that the use of HEVC intra image encoder is efficient in ECG compression. A quantitative performance evaluation was performed and compared to other results found in the literature.

title: A Takagi Sugeno fuzzy neural network based predictive coding scheme for lossless compression of ECG signals
abstract: In this paper, we present an efficient lossless ECG compression method for real time applications. The proposed method hybridizes ECG predictive coding algorithm based on R wave identification and Takagi Sugeno fuzzy neural network. The ECG signal is predicted and encoded by using the periodicity of the electrocardiogram, the correlation between the electrocardiogram signal and the adjacent heartbeat, as well as the continuous characteristic of the ECG waveform. By recording the original prediction errors, non distortion compression of the ECG signal can be achieved. In the experiment, we used MIT BIH arrhythmia ECG database as the test data. According to the experimental results, the proposed algorithm can have a compression ratio up to 3.25 in average, which justifies the usefulness of the proposed approach.

title: High rate compression of ECG signals by an accuracy driven sparsity model relying on natural basis
abstract: Long duration recordings of ECG signals require high compression ratios, in particular when storing on portable devices. Most of the ECG compression methods in literature are based on wavelet transform while only few of them rely on sparsity promotion models. In this paper we propose a novel ECG signal compression framework based on sparse representation using a set of ECG segments as natural basis. This approach exploits the signal regularity, i.e. the repetition of common patterns, in order to achieve high compression ratio (CR) We apply k LiMapS as fine tuned sparsity solver algorithm guaranteeing the required signal reconstruction quality PRDN (Normalized Percentage Root mean square Difference) Extensive experiments have been conducted on all the 48 records of MIT BIH Arrhythmia Database and on some 24 hour records from the Long Term ST Database. Direct comparisons of our method with several state of the art ECG compression methods (namely ARLE, Rajoub's, SPIHT, TRE) prove its effectiveness. Our method achieves average performances that are two three times higher than those obtained by the other assessed methods. In particular the compression ratio gap between our method and the others increases with growing PRDN.

title: The use of an efficient QRS detection algorithm for the compression of ECG signals with large QRS waves
abstract: Recently, the development of storage and transmission technologies and the evolution of information systems, require the handling of large amount of data. Hence, data compression has become essential for both storage and transmission. In this paper, we investigate the influence of an efficient QRS detection technique on the compression quality of a new proposed compression method, in the case of the ECG signals with large QRS waves. The proposed compression method is based on the singular value decomposition and the vector quantization, a compensation technique of the residual reconstruction error is also proposed for the reduction of the reconstruction error. We conducted several tests using arrhythmia database of the Massachusetts Institute of Technology Beth Israel Hospital. The obtained results are very satisfactory, whether in the case of ECG signals with wide or narrow QRS waves. A compression ratio of 102.54 was reached for a low reconstruction error.