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

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 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. Conclusions: 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.