Population based ant colony optimization for reconstructing ECG signals

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


A population based ant colony optimization algorithm (PACO) for the reconstruction of electrocardiogram (ECG) signals is proposed. Specifically, the PACO finds a subset of nonzero positions of a sparse wavelet domain ECG signal vector that is used for the reconstruction of the signal. A time window is used by the proposed PACO for fixing certain decisions of the ants during the run of the algorithm. The optimization behaviour of the PACO is compared with various algorithms from the literature for ECG signal reconstruction, and with two random search heuristics. Experimental results are presented for ECG signals from the MIT-BIT Arrhythmia database. The influence of several algorithmic parameters and of a local search procedure is evaluated. The results show that the proposed PACO algorithm reconstructs ECG signals with high accuracy.

Original languageEnglish
Pages (from-to)55-66
Number of pages12
JournalEvolutionary Intelligence
Issue number3
StatePublished - 1 Sep 2016


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


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