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Entropy 2016, 18(5), 184; doi:10.3390/e18050184

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

1
Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018 , Spain
2
Data and Signal Processing Research Group, University of Vic—Central University of Catalonia, Vic, Catalonia 08500, Spain
3
Escola Superior Politècnica Tecnocampus (UPF), Mataró, Catalonia 08302, Spain
4
BioDonostia Health Institute, Neurology Department Hospital Donostia, Donostia 20014, Spain
This paper is an extended version of one paper published in the 4th IEEE International Work Conference on Bioinspired Intelligence, Donostia, Spain, 9–12 June 2015.
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos M. Travieso-González and Jesús B. Alonso-Hernández
Received: 8 March 2016 / Revised: 4 May 2016 / Accepted: 9 May 2016 / Published: 16 May 2016
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
View Full-Text   |   Download PDF [3710 KB, uploaded 16 May 2016]   |  

Abstract

Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson’s disease. Interestingly, about 50%–70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes’ spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. View Full-Text
Keywords: permutation entropy; essential tremor; automatic drawing analysis; Archimedes’ spiral; non-linear features; automatic feature selection permutation entropy; essential tremor; automatic drawing analysis; Archimedes’ spiral; non-linear features; automatic feature selection
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. (CC BY 4.0).

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MDPI and ACS Style

López-de-Ipiña, K.; Solé-Casals, J.; Faundez-Zanuy, M.; Calvo, P.M.; Sesa, E.; Martinez de Lizarduy, U.; De La Riva, P.; Marti-Masso, J.F.; Beitia, B.; Bergareche, A. Selection of Entropy Based Features for Automatic Analysis of Essential Tremor. Entropy 2016, 18, 184.

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