Multi-Channel ECG Signals Compression Algorithm Using Simultaneous Orthogonal Matching Pursuit

2012 ◽  
Vol 457-458 ◽  
pp. 1305-1309
Author(s):  
Yong Ting Li ◽  
Xiao Yan Chen ◽  
Yue Wen Liu
Keyword(s):  
Signal Processing ◽  
Compression Ratio ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Compression Method ◽  
Petersburg Institute ◽  
Redundant Dictionary ◽  
Compression Performance ◽  
Ecg Signals ◽  
Ecg Compression

Sparse decompression is a new theory for signal processing, having the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of signa1. So the sparse decompression of signal can get sparse representation, which is very important in data compression. In this paper, a novel ECG compression method for multi-channel ECG signals was introduced based on the Simultaneous Orthogonal Matching Pursuit (S-OMP). The proposed method decomposes multi-channel ECG signals simultaneously into different linear expansions of the same atoms that are selected from a redundant dictionary, which is constructed by Hermite fuctions and Gobar functions in order to the best match the characteristic of the ECG waveform. Compression performance has been tested using a subset of multi-channel ECG records from the St.-Petersburg Institute of Cardiological Technics database, the results demonstrate that much less atoms are selected to present signals and the compression ratio of Multi-channel ECG can achieve better performance in comparison to Simultaneous Matching Pursuit (SMP).

scholarly journals Multi-lead ECG Compression Based on Compressive Sensing

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Javad Afshar Jahanshahi
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Matching Pursuit ◽  
Optimization Technique ◽  
Orthogonal Matching Pursuit ◽  
Wavelet Basis ◽  
Basis Matrix ◽  
Ecg Signals ◽  
Gaussian Kernels ◽  
Ecg Compression

Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-lead Electrocardiogram (MECG) signals. This paper develops the compressed sensing theory for sparse modeling and effective multi-channel ECG compression. A basis matrix with Gaussian kernels is proposed to obtain the sparse representation of each channel, which showed the closest similarity to the ECG signals. Thereafter, the greedy orthogonal matching pursuit (OMP) method is used to obtain the sparse representation of the signals. After obtaining the sparse representation of each ECG signal, the compressed sensing theory could be used to compress the signals as much as possible. Following the compression, the compressed signal is reconstructed utilizing the greedy orthogonal matching pursuit (OMP) optimization technique to demonstrate the accuracy and reliability of the algorithm. Moreover, as the wavelet basis matrix is another sparsifying basis to sparse representations of ECG signals, the compressed sensing is applied to the ECG signals using the wavelet basis matrix. The simulation results indicated that the proposed algorithm with Gaussian basis matrix reduces the reconstruction error and increases the compression ratio.

scholarly journals An Enhanced Run-Length Encoding Compression Method for Telemetry Data

2017 ◽  
Vol 24 (3) ◽  
pp. 551-562 ◽  
Author(s):  
Yanhu Shan ◽  
Yongfeng Ren ◽  
Guoyong Zhen ◽  
Kaiqun Wang
Keyword(s):  
Compression Ratio ◽  
High Capacity ◽  
Acquisition System ◽  
Compression Method ◽  
Telemetry Data ◽  
Vibration Signals ◽  
Run Length ◽  
Compression Performance ◽  
Error Factor ◽  
Method Show

AbstractThe telemetry data are essential in evaluating the performance of aircraft and diagnosing its failures. This work combines the oversampling technology with the run-length encoding compression algorithm with an error factor to further enhance the compression performance of telemetry data in a multichannel acquisition system. Compression of telemetry data is carried out with the use of FPGAs. In the experiments there are used pulse signals and vibration signals. The proposed method is compared with two existing methods. The experimental results indicate that the compression ratio, precision, and distortion degree of the telemetry data are improved significantly compared with those obtained by the existing methods. The implementation and measurement of the proposed telemetry data compression method show its effectiveness when used in a high-precision high-capacity multichannel acquisition system.

scholarly journals Hybrid Optical Cryptography and Compressive Sensing Based on Orthogonal Matching Pursuit

2018 ◽  
Author(s):  
Ertan Atar ◽  
Okan Ersoy
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Alternative Solution ◽  
Linear Measurements ◽  
Encrypt Data ◽  
Signal Vector

Compressive sensing (CS) is a technique that allows a signal vector to be reconstructed with a number of linear measurements far fewer than its size. In this study, optical hybrid cryptography with simultaneous compressive sensing based on orthogonal matching pursuit (OMP) is proposed as an alternative solution to the two important problems in communications, namely, effective / efficient / signal processing and secure encryption/decrypton. The whole system is able to both compress and encrypt data with attack immunity.

Sound Power Measurements at Radial Compressors Using Compressed Sensing Based Signal Processing Methods

2019 ◽  
Author(s):  
Jakob Hurst ◽  
Maximilian Behn ◽  
Ulf Tapken ◽  
Lars Enghardt
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Radial Mode ◽  
Mode Analysis ◽  
Sound Power ◽  
Test Rig ◽  
Azimuthal Mode ◽  
Radial Compressor

Abstract Two sound power measurement approaches were developped that are easy to install and have the ability to detect the dominant modal content by applying the modern signal processing method, Compressed Sensing. In general Compressed Sensing requires only few measurement positions for an exact reconstruction of sparse acoustic mode fields. For a current study we have chosen two Compressed Sensing algorithms. Each require separate sensor array arrangements and deliver different modal contents, from which the sound power can be derived. Firstly, an Azimuthal Mode Analysis is conducted by applying the Enhanced Orthogonal Matching Pursuit (EOMP) algorithm to a sound pressure measurement vector. The measurements are obtained by using a sensor ring array with optimized positions. In a subsequent step, the sound power is calculated by referring the detected azimuthal mode spectrum to a model describing the energy distribution over the radial mode content. Secondly, using the Block Orthogonal Matching Pursuit (BOMP) algorithm, the radial mode amplitudes are determined directly. This algorithm requires the sensors to be placed at optimized azimuthal and axial positions and reconstructs a set of dominant radial modes that occur in groups. With the objective to verify both methods, the newly designed and optimized arrays in combination with the aforementioned mode reconstruction algorithms are applied to a numerical data set. This data was provided by URANS simulations of a radial compressor set-up, which is an exact replication of an actual test rig located at the RWTH Aachen University. The introduced estimation methods perform well as shown by comparison with an exact and high resolution Radial Mode Analysis Method. In the near future, the presented measurement approaches will be applied in an experimental study performed at the radial compressor test rig.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

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