PD-ResNet for Classification of Parkinson's Disease From Gait.

ABSTRACT:

Objective

To develop an objective and efficient method to automatically identify Parkinson's disease (PD) and healthy control (HC).

Methods

We design a novel model based on residual network (ResNet) architecture, named PD-ResNet, to learn the gait differences between PD and HC and between PD with different severity levels. Specifically, a polynomial elevated dimensions technique is applied to increase the dimensions of the input gait features; then, the processed data is transformed into a 3-dimensional picture as the input of PD-ResNet. The synthetic minority over-sampling technique (SMOTE), data augmentation, and early stopping technologies are adopted to improve the generalization ability. To further enhance the classification performance, a new loss function, named impro

SUBMITTER: Yang X 

PROVIDER: S-EPMC9252336 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature