PDF(Pubmed)
Abstract：
The purpose of this research is to introduce an approach to assist the diagnosis of Parkinson\'s disease (PD) by classifying functional near-infrared spectroscopy (fNIRS) studies as PD positive or negative. fNIRS is a non-invasive optical signal modality that conveys the brain\'s hemodynamic response, specifically changes in blood oxygenation in the cerebral cortex; and its potential as a tool to assist PD detection deserves to be explored since it is non-invasive and cost-effective as opposed to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance. All the implementations used a logistic regression model for classification. A set of 792 temporal and spectral features were extracted from each participant\'s fNIRS study. In the two best performing implementations, an ensemble of feature-ranking techniques was used to select a reduced feature subset, which was subsequently reduced with a genetic algorithm. Achieving optimal detection performance, our approach reached 100 % accuracy, precision, and recall, with an F1 score and area under the curve (AUC) of 1, using 14 features. This significantly advances PD diagnosis, highlighting the potential of integrating fNIRS and machine learning for non-invasive PD detection.
摘要：
这项研究的目的是介绍一种通过将功能近红外光谱（fNIRS）研究分类为PD阳性或阴性来辅助诊断帕金森病（PD）的方法。fNIRS是一种非侵入性的光学信号模式，传达大脑的血液动力学反应，特别是大脑皮层中血氧合的变化;它作为辅助PD检测的工具的潜力值得探索,因为与其他神经影像学方法相比,它是非侵入性的且具有成本效益。除了fNIRS和机器学习的集成，这项工作的一个贡献是实现和测试了各种方法，以找到实现最高性能的实现。所有实现都使用逻辑回归模型进行分类。从每个参与者的fNIRS研究中提取了一组792个时间和光谱特征。在两个性能最好的实现中，使用特征排序技术的集合来选择减少的特征子集，随后用遗传算法将其缩小。实现最佳检测性能，我们的方法达到了100%的准确度，精度,和回忆，F1评分和曲线下面积（AUC）为1，使用14个特征。这大大促进了PD诊断，强调整合fNIRS和机器学习用于非侵入性PD检测的潜力。
