标题： 基于机器学习的帕金森病运动学特征提取与选择的差异诊断 显示英文标题

标题： Machine Learning-Based Differential Diagnosis of Parkinson's Disease Using Kinematic Feature Extraction and Selection

摘要： 帕金森病（PD）是第二常见的神经退行性疾病，其特征是多巴胺能神经元的丧失和异常α-突触核蛋白的积累。 PD表现出运动和非运动症状，这些症状会逐渐损害日常功能。 这些症状的严重程度通常使用MDS-UPDRS评分量表进行评估，该量表具有主观性，并依赖于医生的经验。 此外，PD与其他神经退行性疾病（如进行性核上性麻痹（PSP）和多系统萎缩（MSA））有相似的症状，这使得准确诊断变得复杂。 为了解决这些诊断挑战，我们提出了一种基于机器学习的系统，用于PD、PSP、MSA和健康对照组（HC）的鉴别诊断。 该系统采用基于运动学特征的分层特征提取和选择方法。 最初提取了18个运动学特征，包括两个新提出的特征：拇指到食指矢量速度和加速度，它们提供了关于运动控制模式的见解。 此外，从每个运动学特征中提取了41个统计特征，包括一些新的方法，如平均绝对变化、节奏、振幅、频率、频率标准差和斜率。 使用单因素方差分析（One-way ANOVA）对特征进行排序，然后使用顺序前向浮点选择（SFFS）来识别最相关的特征，旨在减少计算复杂度。 最终的特征集用于分类，对于每个数据集的分类准确率为66.67%，对于每个患者的分类准确率为88.89%，特别是在MSA和HC组中使用支持向量机（SVM）算法表现尤为出色。 该系统在临床实践中显示出作为快速准确诊断工具的潜力，尽管需要进一步的数据收集和优化以提高其可靠性。 显示英文摘要

摘要： Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterized by dopaminergic neuron loss and the accumulation of abnormal synuclein. PD presents both motor and non-motor symptoms that progressively impair daily functioning. The severity of these symptoms is typically assessed using the MDS-UPDRS rating scale, which is subjective and dependent on the physician's experience. Additionally, PD shares symptoms with other neurodegenerative diseases, such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), complicating accurate diagnosis. To address these diagnostic challenges, we propose a machine learning-based system for differential diagnosis of PD, PSP, MSA, and healthy controls (HC). This system utilizes a kinematic feature-based hierarchical feature extraction and selection approach. Initially, 18 kinematic features are extracted, including two newly proposed features: Thumb-to-index vector velocity and acceleration, which provide insights into motor control patterns. In addition, 41 statistical features were extracted here from each kinematic feature, including some new approaches such as Average Absolute Change, Rhythm, Amplitude, Frequency, Standard Deviation of Frequency, and Slope. Feature selection is performed using One-way ANOVA to rank features, followed by Sequential Forward Floating Selection (SFFS) to identify the most relevant ones, aiming to reduce the computational complexity. The final feature set is used for classification, achieving a classification accuracy of 66.67% for each dataset and 88.89% for each patient, with particularly high performance for the MSA and HC groups using the SVM algorithm. This system shows potential as a rapid and accurate diagnostic tool in clinical practice, though further data collection and refinement are needed to enhance its reliability.