A New Approach for Parkinson's Disease Imaging Diagnosis Using Digitized Spiral Drawing

• Published in: Soft Computing Applications and Techniques in Healthcare pp. 35 - 56
• Main Authors: Kamble, Megha, Patel, Pranshu
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2021; Taylor & Francis Group
• Edition: 1
• Subjects: RF Classifier; F1 Score; Classification SVM; Digitized Tablets; CSV Format; Ml Model; Parkinson's disease classification; PD Diagnosis; Total Time Duration; Dataset II; Random Forest Classifier; Radial Velocity; Machine Learning Models; Hog Feature; Ensemble Model; Spiral Drawing; spiral kinematics; PD Patient; machine learning classification; CSV File; Spiral Test; Medicate PD Patient; PD; LSSVM; Dynamic Spiral; Imbalanced Dataset; Kinematic Features
• ISBN: 9780367552121; 0367423871; 9780367423872; 0367552124
• DOI: 10.1201/9781003003496-3

Parkinson's disease is the most general neurodegenerative disease, upsetting notably motor functions of elderly persons. The diagnosis and monitoring of Parkinson's disease is a costly and inconvenient process even today, in particular, in developing parts of the world. The observable symptoms of Parkinson's disease at early stages include disorders in handwriting and repetitive tasks of spiral drawing. With the advancement of IT, it is easier to collect spiral drawing samples using digitized tablets. We proposed detailed analysis of static and dynamic spirals drawn by Parkinson's disease patients. For this, nearly all kinematic variables are taken out from data files generated for 25 patients and 15 healthy controls, using mathematical models and from PNG drawing files of 15 patients and 15 healthy controls. The second dataset of 30 persons is evaluated with HOG feature engineering and classified using ensemble and machine learning models. Results demonstrated nearly 91% classification accuracy to separate Parkinson's disease patients from healthy controls by applying feature engineering and three machine learning classifiers: Logistic regression, C-support vector classification and ensemble model random forest. This chapter confirms that digitized spiral drawings have a major impact on the classification of Parkinson's disease patients and healthy controls and hence can support future diagnosis and treatment of Parkinson's disease based on data files as well as digital drawings. This chapter confirms that digitized spiral drawings have a major impact on the classification of Parkinson's disease patients and healthy controls and hence can support future diagnosis and treatment of Parkinson's disease based on data files as well as digital drawings. Motor rating scale and its subscale Unified Parkinson's disease rating scale is the popularly used and reliable measuring scale in Parkinson's disease. The scientific method of spiral drawing comprises three types: Stability Test on a Certain Point, spiral test static and dynamic test. Time consumed to complete drawing can be calculated using individual stroke timestamp recorded in the test file with respect to each spiral drawing. The number of strokes is calculated by counting the number of times on-surface pressure is changing during the whole spiral drawing. Spiral drawing dataset consists of spiral and wave drawings of Parkinson's disease patients and healthy controls drawn using digitized tablet.