Contribution of different movement tasks to differential diagnosis of Parkinson’s disease

Abstract

Parkinson's disease is one of the most common neurological diseases, which, according to current knowledge, is incurable. Early detection is essential since, with appropriate therapy and medication, the progress of the disease can be slowed down and the quality of life maintained. The movement tasks described with the acceleration data are part of the intensive research area. This would make recognizing the disease and specific symptoms like tremors, rigidity, and bradykinesia possible. Many research studies focus on the selection of appropriate movement tasks. However, due to the diversity of the studies, no consensus has yet been reached. Therefore, in this research, we examine which of the movement tasks selected from the Unified Parkinson's Disease Rating Scale prove to be the best under the same procedure. Furthermore, we attempted to make a final decision based on the predictions obtained on the movement forms using voting procedures. 37 patients with Parkinson's disease and 47 healthy individuals participated in this study. 3-axial acceleration data from the wrist-mounted sensor was acquired, from which times-series features were determined. Classifications were done by Support Vector Machine. Soft, hard, and SVM-based voting were also explored. The results indicated that the PRONATION task has the highest balanced accuracy (76.2%). Among the voting approaches, the soft achieved the highest improvement (79.8%) compared to the best task. Based on the results, fewer movement tasks can be used to recognize the disease, among which PRONATION is essential. Further voting approaches can improve the performance.