Efficacy of guided spiral drawing in the classification of Parkinson's Disease

Zham, P, Poosapadi Arjunan, S, Raghav, S and Kumar, D 2017, 'Efficacy of guided spiral drawing in the classification of Parkinson's Disease', IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1648-1652.

Document type: Journal Article
Collection: Journal Articles

Title Efficacy of guided spiral drawing in the classification of Parkinson's Disease
Author(s) Zham, P
Poosapadi Arjunan, S
Raghav, S
Kumar, D
Year 2017
Journal name IEEE Journal of Biomedical and Health Informatics
Volume number 22
Issue number 5
Start page 1648
End page 1652
Total pages 6
Publisher IEEE
Abstract 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 67 to 79 %. However, when angular features (φ and pn) and count of direction inversion during sketching of the spiral were used, AUC improved to 93.3%. Spearman correlation coefficient was highest for φ and pn. 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.
Subject Biomedical Instrumentation
Signal Processing
Biomedical Engineering not elsewhere classified
Keyword(s) Parkinson's
Dynamic feature
Kinematic features
DOI - identifier 10.1109/JBHI.2017.2762008
Copyright notice © 2017 IEEE
ISSN 2168-2208
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