Analysis of Augmentations in Contrastive Learning for Parkinson's Disease Diagnosis

Parkinsons disease (PD) is a neurodegenerative disease that causes a movement disorder. Early diagnosis of PD is critical for patients to receive proper treatment, such as levodopa/carbidopa, which are more effective when administered early on at the beginning stage of the disease. However, due to a shortage of experts in the field, a considerable volume of unlabeled data remains unexplored in the existing supervised learning-based PD diagnosis. To fully utilize the available data, we propose a framework to thoroughly evaluate the effect of different data augmentation settings for contrastive learning (CL)-based PD diagnosis. We also provide PD datasets with three modalities (i.e., hand-drawing, speech, and gait) to comprehensively evaluate the detection performance and make them publicly available. Experimental results demonstrate that different augmentation approaches and parameters have a large impact on PD detection performance, and CL could outperform the existing unsupervised deep learning method with proper data augmentation settings. Our study provides insights for researchers in choosing the proper data augmentation and corresponding parameters for CL-based PD diagnosis.