A greedy search algorithm with tree pruning for sparse signal recovery

Lee, Jae-Seok; Kwon, Suhyuk; Shim, Byonghyo

doi:10.1109/isit.2014.6875153

Cited by 1 publication

(2 citation statements)

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“…In order to achieve further reduction in complexity, we also employ a new stopping criterion with marginal performance loss by limiting the minimum pruning threshold. Moreover, compared to [ 31 ], we demonstrate that such modifications not only reduce the search complexity, but also improve the exact recovery condition (ERC) bound. From numerical simulations, we show that our proposed method outperforms the existing methods with practical complexity and provides additional flexibility for hardware implementation.…”

Section: Introductionmentioning

confidence: 94%

“…While the preliminary version of this work was presented for an arbitrary system in [ 31 ], we show that the proposed method is highly suitable for ECG processing with some modifications and performs close to the best possible estimator (the estimator referred to as the oracle least squares (LS) estimator where the support information is given) [ 32 ]. To be specific, we reduced the cardinality of Θ for constructing smaller number of paths in the search tree for real-time implementation.…”

Section: Introductionmentioning

confidence: 99%

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Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements

Lee

Kim

Choi

2017

Sensors

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Due to the necessity of the low-power implementation of newly-developed electrocardiogram (ECG) sensors, exact ECG data reconstruction from the compressed measurements has received much attention in recent years. Our interest lies in improving the compression ratio (CR), as well as the ECG reconstruction performance of the sparse signal recovery. To this end, we propose a sparse signal reconstruction method by pruning-based tree search, which attempts to choose the globally-optimal solution by minimizing the cost function. In order to achieve low complexity for the real-time implementation, we employ a novel pruning strategy to avoid exhaustive tree search. Through the restricted isometry property (RIP)-based analysis, we show that the exact recovery condition of our approach is more relaxed than any of the existing methods. Through the simulations, we demonstrate that the proposed approach outperforms the existing sparse recovery methods for ECG reconstruction.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%