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

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

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).

Artificial Neural Network-based models revealed the best performances when applied to specific phases of the iTUG in differentiating PD vs controls (91 % accuracy) as well as in differentiating cognitive profile (95 % accuracy) and motor response status (96 % accuracy) among PD subjects. K-Nearest Neighbors revealed best performances when applied to EEG data in discriminating PD vs controls (85 % accuracy). Random Forest Classifier revealed best performances when applied to EEG data in differentiating cognitive profile (96 % accuracy) and motor response status (91 % accuracy) among PD subjects.

By processing multimodal instrumental data, specific machine learning algorithms have been identified which discriminated L-dopa responsiveness and cognitive profile in PD. Further studies are needed to validate them in independent samples using a user-friendly software interface created ad hoc.