Testing machine learning algorithms to evaluate fluctuating and cognitive profiles in Parkinson's disease by motion sensors and EEG data.

ABSTRACT:

Objective

We aimed to test machine learning algorithms for classifying fluctuating and cognitive profiles in Parkinson's Disease (PD) by using multimodal instrumental data.

Methods

Data of motion transducers while performing instrumented Timed-Up-and-Go test (iTUG) (N = 30 subjects) and EEG (N = 49 subjects) from PD patients were collected. Study patients were classified based on cognitive profile ("mild cognitive impairment" by standardized criteria vs "normal cognition") and L-dopa acute motor response ("fluctuating" vs "stable") to be analyzed by machine learning algorithms and compared with historical control data from healthy subjects group-matched by age for both iTUG and EEG study (for iTUG: N = 31 subjects; for EEG: N = 27 subjects).

Results

Artificial Neural

SUBMITTER: Mostile G 

PROVIDER: S-EPMC11910500 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature