Detection of Brain Abnormalities in Parkinson’s Rats by Combining Deep Learning and Motion Tracking
Houchi Li, Quan He, Lei Wu
Abstract
Parkinson's disease (PD) is a chronic neurodegenerative disease that affects the central nervous system. PD mainly affects the motor nervous system and may cause cognitive and behavioral problems. One of the best tools to investigate the pathogenesis of PD is animal models, among which the 6-OHDA-treated rat is a widely employed rodent model. In this research, three-dimensional motion capture technology was employed to obtain real-time three-dimensional coordinate information about sick and healthy rats freely moving in an open field. This research also proposes an end-to-end deep learning model of CNN-BGRU to extract spatiotemporal information from 3D coordinate information and perform classification. The experimental results show that the model proposed in this research can effectively distinguish sick rats from healthy rats with a classification accuracy of 98.73%, providing a new and effective method for the clinical detection of Parkinson's syndrome.