A Multiple-Classifier Framework for Parkinson's Disease Detection Based on Various Vocal Tests.
A Multiple-Classifier Framework for Parkinson's Disease Detection Based on Various Vocal Tests.
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DOI：
10.1155/2016/6837498
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发表时间：
2016
影响因子：
4.4
通讯作者：
Sami A
中科院分区：
文献类型：
--
作者：
Behroozi M;Sami A
近年来，语音模式分析在建立帕金森病（PD）预测性远程诊断和远程监护模型中的应用吸引了许多研究者。为此，存在几个语音样本数据集;名为“具有多种类型录音的帕金森语音数据集”的UCI数据集具有各种语音测试，其中包括持续元音，单词，数字和短句，这些短句是从健康和帕金森病患者（PWP）的一组口语练习中编译的。一些研究人员声称，用集中趋势和离散度量来总结每个受试者的多个记录是建立PD预测模型的有效策略。然而，他们忽略了一点，即帕金森病患者可能会表现出更多的困难，在发音某些条款比其他条款。因此，总结声音测试可能导致有价值的信息的丢失。为了解决这一问题，分类设置必须考虑到上述内容。作为解决方案，我们引入了一个新的框架，为每个声乐测试应用独立的分类器。最终的分类结果将是来自所有分类器的多数投票。当我们的方法与基于过滤器的特征选择一起使用时，它将分类准确率提高了15%。
Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson's disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson's disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must take what has been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.