Using Polar Expression Features and Nonlinear Machine Learning Classifier for Automated Parkinson’s Disease Screening

Analyzing digitalized hand-drawn patterns, such as Archimedes' spirals, words, and sentences, is one strategy for evaluating functional tremors and upper-limb movement disorders for neurodegenerative diseases. A pattern, such as a spiral or a line, in polar coordinates is a straight line or a curve that can be easily compared to a hand-drawn pattern with the same coordinates. Hence, in this study, using polar expression features, the deviation (cm), the accumulation angle (rad), and the drawing velocity (cm/s) were extracted to scale the variability in different tremor levels associated with Parkinson's disease (PD) or essential tremor (ET). Then, a nonlinear support vector machine (SVM)-based classifier was used to separate the normal condition from PD or ET. The classifier was trained by the wolf pack search (WPS) optimization method. Tremor level progress was evaluated by integrating an assistive method in a smart mobile device (iPad), along with the nonlinear SVM-based classifier, into a decision-making system for individualized functions. In contrast to the multilayer machine learning method, with the 8-fold cross-validation, the proposed classifier exhibits superior performance in identifying normal controls and PD or ET, with the mean true positive, mean true negative, and mean hit rates being 93.72%, 86.79%, and 90.84%, respectively. The experimental results indicated that the proposed decision-making method is effective for detecting the progression of Parkinson-related diseases. This examination technique is simple, comfortable, and repeatable for helping neurologists with preliminary diagnoses, drug treatments, and at-home monitoring.