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Complexity
Volume 2017 (2017), Article ID 1768264, 13 pages
https://doi.org/10.1155/2017/1768264
Research Article

Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

1BIOtech, Department of Industrial Engineering, University of Trento, Trento, Italy
2Dipartimento di Energia, Ingegneria dell’Informazione e Modelli Matematici (DEIM), University of Palermo, Palermo, Italy
3Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
4Department of Cardiothoracic-Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
5Department of Physiology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Mala Hora 4C, 03601 Martin, Slovakia
6Biomedical Center Martin, Jessenius Faculty of Medicine, Comenius University in Bratislava, Mala Hora 4C, 03601 Martin, Slovakia
7Bruno Kessler Foundation, Trento, Italy

Correspondence should be addressed to Luca Faes; moc.liamg@acul.seaf

Received 18 September 2017; Accepted 13 November 2017; Published 7 December 2017

Academic Editor: Anne Humeau-Heurtier

Copyright © 2017 Luca Faes et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Luca Faes, Alberto Porta, Michal Javorka, and Giandomenico Nollo, “Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models,” Complexity, vol. 2017, Article ID 1768264, 13 pages, 2017. doi:10.1155/2017/1768264