@inproceedings{9d347a9dda00442c98c40966185aad33,
title = "Population based ant colony optimization for reconstructing ECG signals",
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 = "ECG signals, Population based ACO, Signal reconstruction, Subset selection problem",
author = "Cheng, {Yih Chun} and Tom Hartmann and Tsai, {Pei Yun} and Martin Middendorf",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016 ; Conference date: 30-03-2016 Through 01-04-2016",
year = "2016",
doi = "10.1007/978-3-319-31204-0_49",
language = "???core.languages.en_GB???",
isbn = "9783319312033",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "770--785",
editor = "Paolo Burelli and Giovanni Squillero",
booktitle = "Applications of Evolutionary Computation - 19th European Conference, EvoApplications 2016, Proceedings",
}