Computer Science Department, Brock University, St. Catharines, Ontario, Canada.
Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil.
Biosystems. 2023 Oct;232:105006. doi: 10.1016/j.biosystems.2023.105006. Epub 2023 Aug 25.
Parkinson's disease (PD) is a neurodegenerative disease represented by the progressive loss of dopamine producing neurons, with motor and non-motor symptoms that may be hard to distinguish from other disorders. Affecting millions of people across the world, its symptoms include bradykinesia, tremors, depression, rigidity, postural instability, cognitive decline, and falls. Furthermore, changes in gait can be used as a primary diagnosis factor. A dataset is described that records data on healthy individuals and on PD patients, including those who experience freezing of gait, in both the ON and OFF-medication states. The dataset is comprised of data for four separate tasks: voluntary stop, timed up and go, simple motor task, and dual motor and cognitive task. Seven different classifiers are applied to two problems relating to this data. The first problem is to distinguish PD patients from healthy individuals, both overall and per task. The second problem is to determine the effectiveness of medication. A thorough analysis on the classifiers and their results is performed. Overall, multilayer perceptron and decision tree provide the most consistent results.
帕金森病（PD）是一种神经退行性疾病，其特征是多巴胺能神经元进行性丧失，运动和非运动症状可能难以与其他疾病区分。影响着全球数百万人，其症状包括运动迟缓、震颤、抑郁、僵硬、姿势不稳、认知能力下降和跌倒。此外，步态变化可作为主要诊断因素。本文描述了一个数据集，该数据集记录了健康个体和 PD 患者的数据，包括处于 ON 和 OFF 药物状态下经历冻结步态的患者。该数据集由四个独立任务的数据组成：自愿停止、计时起立行走、简单运动任务和双重运动和认知任务。七种不同的分类器应用于与该数据相关的两个问题。第一个问题是区分 PD 患者和健康个体，包括整体和每个任务。第二个问题是确定药物的有效性。对分类器及其结果进行了全面分析。总体而言，多层感知机和决策树提供了最一致的结果。
