通过手写动态与压力特征，用机器学习区分帕金森病患者与健康人。
Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
- 采集帕金森病患者与健康人手写数据，分析运动与压力特征。
- 结合压力特征的SVM模型准确率达81.3%，压力特征单独使用时达82.5%。
- 适合神经科辅助诊断、可穿戴设备开发人员参考。
目的：我们构建了帕金森病手写数据库（PaHaW），包含37名帕金森病患者和38名健康对照者完成的八种手写任务，旨在验证手写运动学特征与压力特征在帕金森病鉴别诊断中的有效性。任务包括绘制阿基米德螺旋、重复书写简单音节与词汇、以及书写句子。除了传统运动学特征，还引入基于书写表面压力的新特征。采用K近邻（K-NN）、AdaBoost集成分类器与支持向量机（SVM）进行对比，结果表明，结合运动学与压力特征的SVM模型表现最佳，分类准确率Pacc=81.3%（敏感性Psen=87.4%，特异性Pspe=80.9%）。单独评估时，压力特征准确率达82.5%，高于仅用运动学特征的75.4%。结论：手写过程中的运动学与压力特征分析有助于捕捉帕金森病细微表现，并有效区分患者与健康人群。
原文摘要 · Abstract (English)
Objective: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and Material: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). Results: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc = 81.3% (sensitivity Psen = 87.4% and specificity of Pspe = 80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc = 82.5% compared to Pacc = 75.4% using kinematic features. Conclusion: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。