MULTISCALE BSBL COMPRESSED SENSING-BASED ECG SIGNAL COMPRESSION WITH ENCODING FOR TELEMEDICINE

2021 ◽  
pp. 2150034
Author(s):  
K. S. Surekha ◽  
B. P. Patil ◽  
Ranjeet Kumar ◽  
Davinder Pal Sharma
Keyword(s):  
Compressed Sensing ◽  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Signal Recovery ◽  
Ecg Signal ◽  
Transmission Cost ◽  
Reconstructed Signal ◽  
Important Diagnostic Tool ◽  
Mean Square Difference

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.

scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

Compressed Sensing Research on Barn Temperature

2012 ◽  
Vol 461 ◽  
pp. 160-163
Author(s):  
Hong Liang Fu ◽  
Hua Wei Tao ◽  
Zheng Luo
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Error ◽  
Information Storage ◽  
Storage Space ◽  
Stored Grain ◽  
Wrong Decision ◽  
Critical Data ◽  
Reconstructed Signal ◽  
Feedback Error

That compressed sensing is used in online monitoring of stored grain information could reduce the mass of information storage space and transmission bandwidth. However, due to the question that compressed sensing reconstructed error may cause decision-end to make wrong decision, a limited feedback error controlling method is proposed, wrong decision-making caused by reconstruction error is solved through feedback a small number of critical data. Numerical experiments on barn temperature shows that this method, on the basis of costing a small amount of compression ratio, can effectively improve the quality of reconstructed signal.

An Effective LFM Signal Reconstruction Method for Signal Denoising

2018 ◽  
Vol 27 (09) ◽  
pp. 1850140
Author(s):  
Shan Luo ◽  
Guoan Bi ◽  
Tong Wu ◽  
Yong Xiao ◽  
Rongping Lin
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Experimental Results ◽  
Signal Denoising ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Time Frequency ◽  
Effective Time ◽  
Reconstructed Signal

One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio (SNR) scenarios. In this paper, we investigate the signal denoising problem for multi-component linear frequency modulated (LFM) signals. An effective time-frequency (TF) analysis-based approach is proposed. Compared to the existing approaches, our proposed one can further increase the noise suppressing performance and improve the quality of the reconstructed signal. Experimental results are presented to show that the proposed denoising approach is able to effectively separate the multi-component LFM signal from the strong noise environments.

scholarly journals Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

2021 ◽  
Vol 12 (1) ◽  
pp. 01-15
Author(s):  
Rui Gong ◽  
Kazunori Hase ◽  
Hajime Ohtsu ◽  
Susumu Ota
Keyword(s):  
Ant Colony Optimization ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Ant Colony ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Snr Improvement ◽  
Mean Square Difference ◽  
Dynamic Filter

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.

Study of Quality Evaluation Indexes in Filtered ECG Signal

2013 ◽  
Vol 284-287 ◽  
pp. 1671-1675
Author(s):  
Gang Zheng ◽  
Ming Li Sun ◽  
Yuan Gu
Keyword(s):  
Quality Evaluation ◽  
Shape Change ◽  
Signal To Noise Ratio ◽  
Ecg Signal ◽  
Voltage Amplitude ◽  
Evaluation Indexes ◽  
St Segment ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) is a kind of weak signal. It was disturbed by surrounding factors, even by patient him/herself. It was happened mostly in portable device. Filtering is an usual step in ECG signal processing. Therefore, the quality evaluation of ECG signal became necessary. In the paper, some indexes were proposed to evaluate the quality of filtered ECG signal. The definition and recommended values or limits of the indexes were discussed. The indexes covered from the aspects of signal procession and clinical diagnosis meanings. They were Signal-to Noise Ratio (SNR), Autocorrelation coefficient (AC), Transformation Ratio (StTR) and Voltage Amplitude Change (StTV) of ECG ST Segment. Median, Wavelet, and Morphology filters were selected in the experiments. From the experiment results, Wavelet performs best in controlling attenuation, but it distorted ST segment the most, both in shape and in its voltage amplitude. The shape change ratio may reach 25%, compare to 17% of median and 14% of morphology, and those filters were acceptable clinical evaluation. It was proved that the indexes can become the potential standard in quality evaluation in ECG signal filtering process.

scholarly journals An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

2019 ◽  
Vol 9 (22) ◽  
pp. 4968 ◽  
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Feng Li ◽  
Jin Wang ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Cardiac Disease ◽  
Signal To Noise Ratio ◽  
Signal Quality ◽  
Ecg Signal ◽  
Mean Square ◽  
Wavelet Coefficients ◽  
Ecg Signals ◽  
Smoothing Filter ◽  
Wavelet Energy ◽  
Mean Square Difference

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

scholarly journals Removal of Structured Noise and Base Line Wander From ECG Signals via LMS Adaptive and Fixed Notch Filter

2018 ◽  
Vol 3 (8) ◽  
pp. 12
Author(s):  
Kawser Ahammed
Keyword(s):  
Signal To Noise Ratio ◽  
Notch Filter ◽  
Ecg Signal ◽  
Base Line ◽  
Mean Square ◽  
Least Mean Square ◽  
Performance Study ◽  
Comparative Performance ◽  
Simulation Results ◽  
Mean Square Difference

This research clearly demonstrates the comparative performance study of Least Mean Square (LMS) adaptive and fixed Notch filter in terms of simulation results and different performance parameters (mean square error, signal to noise ratio and percentage root mean square difference) for removing structured noise (50 Hz line interference and its harmonics) and baseline wandering from electrocardiogram (ECG) signal. The ECG samples collected from the PhysioNet ECG-ID database are corrupted by adding structured noise and base line wandering noise. The simulation results and numerical performance analysis of this research clearly show that LMS adaptive filter can remove noise efficiently from ECG signal than fixed notch filter

A Wi-Fi IoT prototype for ECG monitoring exploiting a novel Compressed Sensing method

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 38
Author(s):  
Eulalia Balestrieri ◽  
Pasquale Daponte ◽  
Luca De Vito ◽  
Francesco Picariello ◽  
Sergio Rapuano ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressed Sensing ◽  
Battery Life ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Ecg Signals ◽  
Reconstruction Quality ◽  
Acquisition Device ◽  
Iot Devices

<p><span lang="EN-US">The paper presents an Internet of Things (IoT) prototype which consists of a data acquisition device wirelessly connected to Internet via Wi-Fi, for continuous electrocardiogram (ECG) monitoring. The proposed system performs a novel Compressed Sensing (CS) based method on ECG signal with the aim of reducing the amount of transmitted data, thus realizing an efficient way to increase the battery life of such devices. For the assessment of the energy consumption of the device, an experimental setup was arranged and its description is presented. The evaluation of the reconstruction quality of the ECG signal in terms of Percentage of Root-mean-squared Difference (PRD</span><span lang="EN-US">) is reported for several Compression Ratios (CRs</span><span lang="EN-US">). The obtained experimental results clearly demonstrate the robustness and usefulness of the Wi-Fi based IoT devices adopting the considered CS-method for data compression of ECG signals. Furthermore, it allows reducing the energy consumption of the IoT device, by increasing the CR</span><span lang="EN-US">, without significantly degrading the quality of the reconstructed ECG signal.</span></p>

scholarly journals An efficient algorithm for compression-based compressed sensing

2018 ◽  
Vol 8 (2) ◽  
pp. 343-375 ◽  
Author(s):  
Sajjad Beygi ◽  
Shirin Jalali ◽  
Arian Maleki ◽  
Urbashi Mitra
Keyword(s):  
Compressed Sensing ◽  
Video Compression ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Robustness Analysis ◽  
Rate Distortion ◽  
Low Complexity ◽  
Signal Recovery ◽  
Recovery Algorithm

Abstract Modern image and video compression codes employ elaborate structures in an effort to encode them using a small number of bits. Compressed sensing (CS) recovery algorithms, on the other hand, use such structures to recover the signals from a few linear observations. Despite the steady progress in the field of CS, the structures that are often used for signal recovery are still much simpler than those employed by state-of-the-art compression codes. The main goal of this paper is to bridge this gap by answering the following question: can one employ a compression code to build an efficient (polynomial time) CS recovery algorithm? In response to this question, the compression-based gradient descent (C-GD) algorithm is proposed. C-GD, which is a low-complexity iterative algorithm, is able to employ a generic compression code for CS and therefore enlarges the set of structures used in CS to those used by compression codes. Three theoretical contributions are provided: a convergence analysis of C-GD, a characterization of the required number of samples as a function of the rate-distortion function of the compression code and a robustness analysis of C-GD to additive white Gaussian noise and other non-idealities in the measurement process. Finally, the presented simulation results show that, in image CS, using compression codes such as JPEG2000, C-GD outperforms state-of-the-art methods, on average, by about $2$–$3$ dB in peak signal-to-noise ratio.

scholarly journals Compressed Sensing Speech Signal Enhancement Research

2017 ◽  
Vol 6 (1) ◽  
pp. 26
Author(s):  
Kuangfeng Ning ◽  
Guojun Qin
Keyword(s):  
Compressed Sensing ◽  
Compressive Sensing ◽  
Input Signal ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Sampling Theorem ◽  
Compressed Domain ◽  
Signal To Noise ◽  
Perceptual Evaluation

<span lang="EN-US">The proposed Compressive sensing method is a new alternative method</span><span lang="EN-US">, it is</span><span lang="EN-US"> used to eliminate noise from the input signal</span><span lang="EN-US">,</span><span lang="EN-US"> and the quality of the speech signal </span><span lang="EN-US">is </span><span lang="EN-US">enhance</span><span lang="EN-US">d</span><span lang="EN-US"> with fewer samples</span><span lang="EN-US">, thus it is</span><span lang="EN-US"> required for the reconstruction than needed in some of the methods like Nyquist sampling theorem. The basic idea is</span><span lang="EN-US"> that </span><span lang="EN-US">the speech signals are sparse in nature</span><span lang="EN-US">,</span><span lang="EN-US"> and most of the noise signals are non-sparse in nature, and Compressive </span><span lang="EN-US">S</span><span lang="EN-US">ensing</span><span lang="EN-US">(</span><span lang="EN-US">CS) eliminates the non-sparse components and </span><span lang="EN-US">it </span><span lang="EN-US">reconstructs only the sparse components of the input signal. Experimental results prove that the average segmental SNR (signal to noise ratio) and PESQ (perceptual evaluation of speech quality) scores are better in the compressed domain</span><span lang="EN-US">．</span>

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