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

The balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compressed sensing (CS) based system is presented as a specialized recovering tool for Electrocardiograph (ECG) signals. Being based on the standard OMP approach, bWOMP is a lightweight reconstruction algorithm both in terms of complexity and memory footprint. Furthermore, the concept of weighting is introduced in the algorithm by exploring a prior knowledge on ECG signals. Experimental results show a performance increase of about 10 dB with respect to the standard OMP approach, and also an increase with respect to the decoding approaches considered as the state-of-the-art. In this case the gain could be as high as 4 dB with respect to the best of currently known decoding approaches.

[1]  Riccardo Rovatti,et al.  Enhanced rake receivers for chaos-based DS-CDMA , 2001 .

[2]  Pei-Yun Tsai,et al.  Matrix-Inversion-Free Compressed Sensing With Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals , 2016, IEEE Transactions on Biomedical Circuits and Systems.

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

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

[5]  Claudio Pollo,et al.  Compact Low-Power Cortical Recording Architecture for Compressive Multichannel Data Acquisition , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[6]  Daibashish Gangopadhyay,et al.  Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[7]  Riccardo Rovatti,et al.  Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[8]  Gianluca Mazzini,et al.  Performance of chaos-based asynchronous DS-CDMA with different pulse shapes , 2004, IEEE Communications Letters.

[9]  Luca Benini,et al.  Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks , 2017, Microprocess. Microsystems.

[10]  Riccardo Rovatti,et al.  Rakeness-Based Design of Low-Complexity Compressed Sensing , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[12]  Refet Firat Yazicioglu,et al.  An Efficient and Compact Compressed Sensing Microsystem for Implantable Neural Recordings , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[13]  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.

[14]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[15]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[16]  Jun Zhang,et al.  Energy-Efficient ECG Compression on Wireless Biosensors via Minimal Coherence Sensing and Weighted $\ell_1$ Minimization Reconstruction , 2015, IEEE Journal of Biomedical and Health Informatics.

[17]  Daibashish Gangopadhyay,et al.  Compressed Sensing Analog Front-End for Bio-Sensor Applications , 2014, IEEE Journal of Solid-State Circuits.