Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs

Marchioni, Alex; Mangia, Mauro; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca

doi:10.1109/biocas.2017.8325143

Cited by 2 publications

(4 citation statements)

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“…The acquired CS measurements were quantized using 11 bits to better emulate realistic systems. For DWT basis, we used Symlet-6 wavelet with 6 levels of decomposition, following the setting used for WLM and WOMP in [21], [23]. Available ECG records were divided into frames of length N each.…”

Section: Resultsmentioning

confidence: 99%

“…Numerical results for CS-ECG decoders included in our experiments were produced by the following publicly available MATLAB-based solvers: SPGL1 solver for BPDN [53], BSBL_BO solver for BSBL [54], l1-ls solver for the lasso formulation used in WLM [55], and OMP solver from Sparselab toolbox [56] for WOMP, with applying relevant modifications according to [23]. Finally, all numerical experiments were performed by using MATLAB 2018a running on a desktop computer equipped with an octa-core Intel i7-10700 pro-TABLE 1.…”

Section: Resultsmentioning

confidence: 99%

“…The above factors have lead FCE to achieve improvements over WLM in terms of reconstruction quality and speed. The same arguments apply to the WOMP technique [23], which is an adaptation of WLM to the OMP algorithm. We verify the advantages of FCE over WLM and WOMP and other decoding techniques through numerical experiments in the next section.…”

Section: Comparison To Weighted 1 Minimization Techniquementioning

confidence: 95%

“…Orthogonal Matching Pursuit (OMP) [22] is a widely used fast CS decoding algorithm. Weighted OMP (WOMP) in [23] adapted the WLM technique to OMP in order to benefit from the speed advantage of OMP.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

Rateb

2020

IEEE Access

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Compressed Sensing (CS) has been proposed as a low-complexity ECG data compression scheme for wearable wireless bio-sensor devices. However, CS decoding is characterized by high computational complexity. As a result, it represents a burden to the computational and energy resources of the network gateway node, where decoding is performed. In this article, we propose a Fast Compressive Electrocardiography (FCE) technique to address this problem. CS decoding in FCE is based on Weighted Regularized Least-Squares (WRLS), rather than the standard approach based on 1 norm minimization. The WRLS formulation takes into account prior knowledge of ECG signal properties to estimate an optimally compact and accurate representation of ECG signals. Numerical results show that decoding by FCE is on average 33 times faster than the fastest tested CS-based ECG decoding technique. In addition, highquality ECG signal reconstruction by FCE is achieved at 32% higher compression ratio. Therefore, FCE can contribute to improving the overall energy and computational resource efficiency of CS-based remote ECG monitoring systems.INDEX TERMS Compressed sensing (CS), electrocardiogram (ECG), random demodulator, remote health monitoring, wireless body-sensor network (WBSN).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Comparison To Weighted 1 Minimization Techniquementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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