Spiral analysis-improved clinical utility with center detection

Spiral analysis is a computerized method that measures human motor performance from handwritten Archimedean spirals. It quantifies normal motor activity, and detects early disease as well as dysfunction in patients with movement disorders. The clinical utility of spiral analysis is based on kinematic and dynamic indices derived from the original spiral trace, which must be detected and transformed into mathematical expressions with great precision. Accurately determining the center of the spiral and reducing spurious low frequency noise caused by center selection error is important to the analysis. Handwritten spirals do not all start at the same point, even when marked on paper, and drawing artifacts are not easily filtered without distortion of the spiral data and corruption of the performance indices. In this report, we describe a method for detecting the optimal spiral center and reducing the unwanted drawing artifacts. To demonstrate overall improvement to spiral analysis, we study the impact of the optimal spiral center detection in different frequency domains separately and find that it notably improves the clinical spiral measurement accuracy in low frequency domains.