Population Based Ant Colony Optimization for Reconstructing ECG Signals

A population based ant optimization algorithm (PACO) for reconstructing electrocardiogram (ECG) signals is proposed in this paper. In particular, the PACO algorithm is used to find a subset of nonzero positions of a sparse wavelet domain ECG signal vector which is used for the reconstruction of a signal. The proposed PACO algorithm uses a time window for fixing certain decisions of the ants during the run of the algorithm. The optimization behaviour of the PACO is compared with two random search heuristics and several algorithms from the literature for ECG signal reconstruction. Experimental results are presented for ECG signals from the MIT-BIT Arrhythmia database. The results show that the proposed PACO reconstructs ECG signals very successfully.

[1]  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.

[2]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[3]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[4]  Manuel Blanco-Velasco,et al.  Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems , 2014, IEEE Journal of Biomedical and Health Informatics.

[5]  Pierre Vandergheynst,et al.  Structured sparsity models for compressively sensed electrocardiogram signals: A comparative study , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[6]  James S. Walker,et al.  A Primer on Wavelets and Their Scientific Applications, Second Edition , 2008 .

[7]  Christine Solnon,et al.  An Ant Colony Optimization Meta-Heuristic for Subset Selection Problems , 2006 .

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

[9]  Pei-Yun Tsai,et al.  Low-complexity compressed sensing with variable orthogonal multi-matching pursuit and partially known support for ECG signals , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

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

[11]  Martin Middendorf,et al.  A Population Based Approach for ACO , 2002, EvoWorkshops.

[12]  Daibashish Gangopadhyay,et al.  Compressed sensing reconstruction: Comparative study with applications to ECG bio-signals , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[13]  Nadia Abd-Alsabour,et al.  Binary Ant Colony Optimization for Subset Problems , 2015, Multi-objective Swarm Intelligence.

[14]  T. Blumensath,et al.  On the Difference Between Orthogonal Matching Pursuit and Orthogonal Least Squares , 2007 .

[15]  Lenka Lhotská,et al.  Ant Colony Cooperative Strategy in Electrocardiogram and Electroencephalogram Data Clustering , 2007, NICSO.

[16]  Martin Middendorf,et al.  Simple Probabilistic Population-Based Optimization , 2016, IEEE Transactions on Evolutionary Computation.

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

[18]  Thomas Stützle,et al.  A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP , 2011, GECCO.

[19]  Bernd Scheuermann,et al.  FPGA implementation of population-based ant colony optimization , 2004, Appl. Soft Comput..

[20]  Hartmut Schmeck,et al.  Ant colony optimization for resource-constrained project scheduling , 2000, IEEE Trans. Evol. Comput..

[21]  Paul S Addison,et al.  Wavelet transforms and the ECG: a review , 2005, Physiological measurement.

[22]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[23]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[24]  Martin Middendorf,et al.  Flexible particle swarm optimization tasks for reconfigurable processor arrays , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[25]  Jian Wang,et al.  Generalized Orthogonal Matching Pursuit , 2011, IEEE Transactions on Signal Processing.

[26]  Zbigniew Michalewicz,et al.  Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem , 2014, IEEE Computational Intelligence Magazine.

[27]  R. Sivakumar,et al.  Ant-based Clustering Algorithms: A Brief Survey , 2010 .

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

[29]  James S. Walker,et al.  A Primer on Wavelets and Their Scientific Applications , 1999 .