Population based ant colony optimization for reconstructing ECG signals

Yih Chun Cheng, Tom Hartmann, Pei Yun Tsai, Martin Middendorf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 19th European Conference, EvoApplications 2016, Proceedings
EditorsPaolo Burelli, Giovanni Squillero
PublisherSpringer Verlag
Pages770-785
Number of pages16
ISBN (Print)9783319312033
DOIs
StatePublished - 2016
Event19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016 - Porto, Portugal
Duration: 30 Mar 20161 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9597
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016
Country/TerritoryPortugal
CityPorto
Period30/03/161/04/16

Keywords

  • ECG signals
  • Population based ACO
  • Signal reconstruction
  • Subset selection problem

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