Spadoto Andre A, Guido Rodrigo C, Papa Joao P, Falcao Alexandre X
Institute of Physics at São Carlos, University of São Paulo, São Carlos, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6087-90. doi: 10.1109/IEMBS.2010.5627634.
Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification.
人工智能技术已被广泛用于识别与语音信号分析相关的多种疾病，如帕金森病（PD）。然而，其中一些技术存在缺陷，因为它们在原始特征空间甚至核映射所诱导的特征空间中假设了某种可分离性。在本文中，我们提出了通过最优路径森林（OPF）进行帕金森病自动识别，最优路径森林是一种新开发的模式识别技术，它不假设类/特征空间的任何形状/可分离性。实验表明，在帕金森病识别方面，最优路径森林优于支持向量机、人工神经网络和其他常用的监督分类技术。
