Efficacy of Guided Spiral Drawing in the Classification of Parkinson's Disease

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
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:22 (5): 1648-1652 被引量:86
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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