Classification of Parkinson's disease stages with a two-stage deep neural network.

ABSTRACT:

Introduction

Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test.

Methods

The proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the acti

SUBMITTER: Pedrero-Sanchez JF 

PROVIDER: S-EPMC10272759 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature