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Sensors 2017, 17(1), 105; doi:10.3390/s17010105

Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements

Department of Information & Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 771-813, Korea
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 24 October 2016 / Revised: 18 December 2016 / Accepted: 3 January 2017 / Published: 7 January 2017
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
View Full-Text   |   Download PDF [561 KB, uploaded 17 January 2017]   |  


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. View Full-Text
Keywords: biomedical signal processing; electrocardiogram; compressed sensing; sparse signal recovery; tree pruning biomedical signal processing; electrocardiogram; compressed sensing; sparse signal recovery; tree pruning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lee, J.; Kim, K.; Choi, J.-W. Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements. Sensors 2017, 17, 105.

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