Comparison of Four Recovery Algorithms Used in Compressed Sensing for ECG Signal Processing

Zhang, Zhimin; Wei, Shoushui; Wei, Dingwen; Li, Liping; Liu, Feng; Liu, Chengyu

doi:10.22489/cinc.2016.116-226

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…In this section, we simulated and compared some of the architectures proposed in [ 9 , 18 , 19 , 20 ] in Figure 5 . The architecture proposed in [ 18 ] (referred to as the orthogonal matching pursuit, OMP) recovers a signal using a Gaussian random sensing matrix, and the architecture proposed in [ 19 ] and [ 20 ] are bound-optimization-based block sparse Bayesian learning (BSBL-BO) and expectation-maximum-based block sparse Bayesian learning (BSBL-EM) respectively. The sensing matrix utilizing in BSBL-BO and BSBL-EM are randomly generated sparse binary sensing matrix, with each column consisting of 12 entries of 1 s with random locations, while other entries were all zero [ 19 ].…”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

Tseng

Chen

2017

Sensors

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Compressed sensing (CS) is a promising approach to the compression and reconstruction of electrocardiogram (ECG) signals. It has been shown that following reconstruction, most of the changes between the original and reconstructed signals are distributed in the Q, R, and S waves (QRS) region. Furthermore, any increase in the compression ratio tends to increase the magnitude of the change. This paper presents a novel approach integrating the near-precise compressed (NPC) and CS algorithms. The simulation results presented notable improvements in signal-to-noise ratio (SNR) and compression ratio (CR). The efficacy of this approach was verified by fabricating a highly efficient low-cost chip using the Taiwan Semiconductor Manufacturing Company’s (TSMC) 0.18-μm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The proposed core has an operating frequency of 60 MHz and gate counts of 2.69 K.

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Section: Simulation Resultsmentioning

confidence: 99%

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

Tseng

Chen

2017

Sensors

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“…• BSBL-BO (Block Sparse Bayesian Learning-Bound Optimization) [13], [15], [26] • CS-NET [16] Once each window has been reconstructed, they are flattened to form the sequence of reconstructed samples.…”

Section: Decodingmentioning

confidence: 99%

A Compressive Sensing Codec Architecture for Ecg Signals with Adaptive Quantization and Stream Entropy Coding

Kumar

Prasad

Lall

2023

Preprint

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“…In addition to the predefined dictionaries, a subclass of dictionaries are employed to represent signals in sparse basis [20]. These dictionaries have made significant contributions to the field of sparse signal processing due to their effectiveness in sparse signal representation [21] at the cost of more training data and computational resources.…”

Section: Wavelet Coefficientsmentioning

confidence: 99%

“…A wide range of CS based optimization algorithms for ECG signal reconstruction are investigated and compared with the Bayes' theorem [57]. It demonstrates that Bayesian recovery outperforms a range of traditional recovery techniques.…”

Section: ) Block Compressed Sensingmentioning

confidence: 99%

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

et al. 2022

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Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.

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Comparison of Four Recovery Algorithms Used in Compressed Sensing for ECG Signal Processing

Cited by 11 publications

References 6 publications

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

A Compressive Sensing Codec Architecture for Ecg Signals with Adaptive Quantization and Stream Entropy Coding

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

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