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.
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Acknowledgments
YCC received financial support granted by German Academic Exchange Service (DAAD) through the Taiwan Summer Institute Programme within 57190416. TH was funded by the German Israeli Foundation (GIF) through the project “Novel gene order analysis methods based on pattern identification in gene interaction networks” within G-1051-407.4-2013.
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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. https://doi.org/10.1007/978-3-319-31204-0_49
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DOI: https://doi.org/10.1007/978-3-319-31204-0_49
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