斯皮尔曼秩相关系数
秩相关
相关性
螺旋(铁路)
数学
帕金森病
相关系数
曲线下面积
队列
笔迹
统计
组合数学
医学
人工智能
数学分析
疾病
几何学
计算机科学
内科学
药代动力学
作者
Poonam Zham,Sridhar P. Arjunan,Sanjay Raghav,Dinesh Kumar
标识
DOI:10.1109/jbhi.2017.2762008
摘要
BACKGROUND: Change of handwriting can be an early marker for severity of Parkinson's disease but suffers from poor sensitivity and specificity due to inter-subject variations. AIM: This study has investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients. METHOD: Dynamic handwriting features were computed for 206 specimens collected from 62 Subjects (31 Parkinson's and 31 Controls). These were analyzed based on the severity of the disease to determine group-difference. Spearman rank correlation coefficient was computed to evaluate the strength of association for the different features. RESULTS: Maximum area under ROC curve (AUC) using the dynamic features during different writing and spiral sketching tasks were in the range of 0.67 to 0.79. However, when angular features ($\boldsymbol{\varphi }$ and ${\boldsymbol{p}_{\boldsymbol{n}}}$) and count of direction inversion during sketching of the spiral were used, AUC improved to 0.933. Spearman correlation coefficient was highest for ϕ and ${\boldsymbol{p}_{\boldsymbol{n}}}$. CONCLUSION: The angular features and count of direction inversion which can be obtained in real-time while sketching the Archimedean guided spiral on a digital tablet can be used for differentiating between Parkinson's and healthy cohort.
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