Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements

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.

[1]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[2]  E. Candès The restricted isometry property and its implications for compressed sensing , 2008 .

[3]  Bhaskar D. Rao,et al.  Application of tree-based searches to matching pursuit , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[4]  Jian Wang,et al.  Multipath Matching Pursuit , 2013, IEEE Transactions on Information Theory.

[5]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[6]  Justin Ziniel,et al.  Fast bayesian matching pursuit , 2008, 2008 Information Theory and Applications Workshop.

[7]  G. Moody,et al.  The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. , 1992, European heart journal.

[8]  D. Atar,et al.  ESC Guidelines for the Management of Acute Myocardial Infarction in Patients Presenting With ST-Segment Elevation , 2013 .

[9]  Tzyy-Ping Jung,et al.  Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning , 2012, IEEE Transactions on Biomedical Engineering.

[10]  Rina Dechter,et al.  Generalized best-first search strategies and the optimality of A* , 1985, JACM.

[11]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[12]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[13]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[14]  Byonghyo Shim,et al.  A greedy search algorithm with tree pruning for sparse signal recovery , 2014, 2014 IEEE International Symposium on Information Theory.

[15]  U. Fincke,et al.  Improved methods for calculating vectors of short length in a lattice , 1985 .

[16]  Mrityunjoy Chakraborty,et al.  Improving the Bound on the RIP Constant in Generalized Orthogonal Matching Pursuit , 2013, IEEE Signal Processing Letters.

[17]  G. Karabulut,et al.  Integrating flexible tree searches to orthogonal matching pursuit algorithm , 2006 .

[18]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[19]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[20]  Hakan Erdogan,et al.  A* orthogonal matching pursuit: Best-first search for compressed sensing signal recovery , 2010, Digit. Signal Process..

[21]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[22]  Roger Abächerli,et al.  Embroidered Electrode with Silver/Titanium Coating for Long-Term ECG Monitoring , 2015, Sensors.

[23]  Frederick Jelinek,et al.  Statistical methods for speech recognition , 1997 .

[24]  Byonghyo Shim,et al.  Soft-Input Soft-Output List Sphere Detection with a Probabilistic Radius Tightening , 2012, IEEE Transactions on Wireless Communications.

[25]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[26]  Byonghyo Shim,et al.  Sphere Decoding With a Probabilistic Tree Pruning , 2008, IEEE Transactions on Signal Processing.

[27]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[28]  Giancarlo Agnelli,et al.  96 hours ECG monitoring for patients with ischemic cryptogenic stroke or transient ischaemic attack , 2014, Internal and Emergency Medicine.

[29]  Chris Van Hoof,et al.  Soft, Comfortable Polymer Dry Electrodes for High Quality ECG and EEG Recording , 2014, Sensors.

[30]  Liam Kilmartin,et al.  Compressed Sensing for Bioelectric Signals: A Review , 2015, IEEE Journal of Biomedical and Health Informatics.

[31]  Nigel H. Lovell,et al.  Low-power technologies for wearable telecare and telehealth systems: A review , 2015 .

[32]  Miguel R. D. Rodrigues,et al.  Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach , 2013, IEEE Transactions on Signal Processing.

[33]  Stephan ten Brink,et al.  Achieving near-capacity on a multiple-antenna channel , 2003, IEEE Trans. Commun..

[34]  Hakan Erdogan,et al.  Improving A⋆ OMP: Theoretical and empirical analyses with a novel dynamic cost model , 2013, Signal Process..

[35]  Dilbag Singh,et al.  Ectopic beats in approximate entropy and sample entropy-based HRV assessment , 2012, Int. J. Syst. Sci..

[36]  Miquel Fiol,et al.  ECG Diagnosis and Classification of Acute Coronary Syndromes , 2014, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[37]  Pierre Vandergheynst,et al.  Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes , 2011, IEEE Transactions on Biomedical Engineering.