Selection of entropy based features for the analysis of the Archimedes' spiral applied to essential tremor

2015 4th International Work Conference on Bioinspired Intelligence (IWOBI) Pub Date : 2015-06-10 DOI:10.1109/IWOBI.2015.7160160

K. López de Ipiña, M. Iturrate, P. Calvo, B. Beitia, J. Garcia-Melero, A. Bergareche, P. de la Riva, J. Martí‐Massó, M. Faúndez-Zanuy, E. Sesa-Nogueras, J. Roure, Jordi Solé-Casals

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引用次数: 10

Abstract

Biomedical systems are regulated by interacting mechanisms that operate across multiple spatial and temporal scales and produce biosignals with linear and non-linear information inside. In this sense entropy could provide a useful measure about disorder in the system, lack of information in time-series and/or irregularity of the signals. Essential tremor (ET) is the most common movement disorder, being 20 times more common than Parkinson's disease, and 50-70% of this disease cases are estimated to be genetic in origin. Archimedes spiral drawing is one of the most used standard tests for clinical diagnosis. This work, on selection of nonlinear biomarkers from drawings and handwriting, is part of a wide-ranging cross study for the diagnosis of essential tremor in BioDonostia Health Institute. Several entropy algorithms are used to generate nonlinear feayures. The automatic analysis system consists of several Machine Learning paradigms.

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选择基于熵的特征来分析应用于特发性震颤的阿基米德螺旋

生物医学系统受到相互作用机制的调控，这些机制在多个时空尺度上运作，并产生包含线性和非线性信息的生物信号。从这个意义上说，熵可以提供一个有用的测量系统的无序，缺乏信息的时间序列和/或不规则的信号。特发性震颤(ET)是最常见的运动障碍，比帕金森氏病多20倍，估计50-70%的该病病例是遗传的。阿基米德螺旋图是临床上常用的标准诊断方法之一。这项从绘画和笔迹中选择非线性生物标志物的工作，是BioDonostia健康研究所广泛的特发性震颤诊断交叉研究的一部分。使用了几种熵算法来生成非线性特征。自动分析系统由几个机器学习范例组成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)

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