Distributed compressive sensing for multichannel ECG signals over learned dictionaries

Singh, Anurag; Dandapat, Samarendra

doi:10.1109/indicon.2014.7030638

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…In this correspondence, Anurag Singh and S. Dandapat et al [13], have proposed and talk about similarly a few procedures for ECG signal compression propelled from the basics of CS theory, concentrating on securing systems, projection lattices and remaking word references and on the impacts of the preprocessing included. The primary methodology for ECG signal compression depends on the immediate CS securing of the signal with no preprocessing of the waveforms before taking the projections, neither for the development of the word references.…”

Section: Data Acquisition Techniquesmentioning

confidence: 99%

“…In this work, Anurag Singh et al [15], proposed a Distributed Compressive Sensing (DCS) is to develop the fundamental connection structure between various channels of Multi-channel ECG signals. The joint remaking capacity of DCS lessens the number of compacted estimations required for precise reproduction without influencing the contortion level.…”

Section: Data Acquisition Techniquesmentioning

confidence: 99%

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Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Mishra¹,

Jain

2020

Journal of Intelligent Systems

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In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.

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Section: Data Acquisition Techniquesmentioning

confidence: 99%

Section: Data Acquisition Techniquesmentioning

confidence: 99%

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Mishra¹,

Jain

2020

Journal of Intelligent Systems

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“…The third model can be considered as a modified version of the first model. The second joint sparsity model (JSM‐2) has been actively explored in many existing literature . A joint orthogonal matching pursuit for DCS was proposed to improve the target detection performance of MIMO radar .…”

Section: Introductionmentioning

confidence: 99%

“…The DCS was shown to be feasible for a realistic wireless sensor WSNs by implementing on real commercial off‐the‐shelf hardware with providing good trade‐off between performance and energy consumption . Exploiting common information across the multiple electrocardiogram (ECG) signals, simultaneous orthogonal matching pursuit for DCS with learned dictionary was shown to provide accurate reconstruction with the reduced number of measurements . However, the first model (JSM‐1) has been studied relatively little to the best of authors' knowledge.…”

Section: Introductionmentioning

confidence: 99%

Generalized distributed compressive sensing with security challenges for linearly correlated information sources

Park

Hwang²,

Yang

et al. 2017

Concurrency and Computation

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Distributed compressive sensing (DCS) usually improves the signal recovery performance of multi-signal ensembles by exploiting both intra-and inter-signal correlation and sparsity structure. However, the existing DCS had proposed for a very limited ensemble of signals that has only single common information. This paper proposes a generalized DCS (GDCS) framework which can improve sparse signal detection performance given arbitrary types of common information, which are classified into full common information and partial common information after overcoming against existing limitation. Specifically, the theoretical bound on the required number of measurements under the GDCS is obtained. We also develop a practical algorithm to obtain benefits using the GDCS. At the end of this paper, it simply summarizes the potential security issues when it gets all sensing information in a sensor network. Finally, numerical results verify that the proposed algorithm reduces the required number of measurements for correlated sparse signal detection compared to the DCS algorithm. This research lays down the basis for efficient distributed signal detection so that it can improve the detection performance or it can detect the signal reliably when the number of signal observations is limited. KEYWORDS compressive sensing, distributed source coding, security sensor networks, sparsity 1 INTRODUCTION Baron et al 1 introduced distributed compressive sensing (DCS), which exploits not only just intra-correlation but also inter-correlation of signals to improve detection performance. In the work of Baron et al, 1 it assumed wireless sensor network (WSN) consisting of an arbitrary number of sensors and one sink node, where each sensor carries out compression without the cooperation of the other sensors and transmits the compressed signal to the sink node. At the sink node, it jointly reconstructs the original signals from the received signals. Here, a key of DCS is a concept of joint sparsity, defined as the sparsity of the entire signal ensemble. Three models have been considered as a joint sparse signal model in the work of Baron et al. 1 In the first model, each signal is individually sparse, and also there are common components shared by every signal vector, and it is called common information. In the second model, all signals share supports that are the locations of the nonzero coefficients. In the third model, although no signal is sparse itself, they share the large amount of common information which makes it possible to compress and recover the signals using CS. The third model can be considered as a modified version of the first model. The second joint sparsity model (JSM-2) has been actively explored in many existing literature. 2-8 A joint orthogonal matching pursuit for DCS was proposed to improve the target detection performance of MIMOradar. 4 A precognition matching pursuit which used the knowledge of common support from Frechet mean was proposed to reduce the number of required measurements in WSNs. 5 The DCS was shown to be f...

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Distributed Compressive Sensing for Correlated Information Sources

Park

Hwang

Yang

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Distributed compressive sensing for multichannel ECG signals over learned dictionaries

Cited by 5 publications

References 18 publications

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

Generalized distributed compressive sensing with security challenges for linearly correlated information sources

Distributed Compressive Sensing for Correlated Information Sources

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