Population Based Ant Colony Optimization for Reconstructing ECG Signals

  • Yih-Chun Cheng
  • Tom Hartmann
  • Pei-Yun Tsai
  • Martin Middendorf
Conference paper

DOI: 10.1007/978-3-319-31204-0_49

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)
Cite this paper as:
Cheng YC., Hartmann T., Tsai PY., Middendorf M. (2016) Population Based Ant Colony Optimization for Reconstructing ECG Signals. In: Squillero G., Burelli P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science, vol 9597. Springer, Cham

Abstract

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.

Keywords

Population based ACO ECG signals Signal reconstruction Subset selection problem 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yih-Chun Cheng
    • 2
  • Tom Hartmann
    • 1
  • Pei-Yun Tsai
    • 2
  • Martin Middendorf
    • 1
  1. 1.Parallel Computing and Complex Systems Group, Institute of Computer ScienceUniversity LeipzigLeipzigGermany
  2. 2.Department of Electrical EngineeringNational Central UniversityTaoyuan CityTaiwan

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