Linear predictive coding electroencephalography algorithms predict Parkinson's disease mortality using out-of-sample tests

BackgroundParkinsons disease (PD) increases mortality is difficult to predict because of its heterogeneity and the availability of very few reliable which prognostic markers. ObjectivesWe used electroencephalography (EEG) and the Linear Predictive Coding EEG Algorithm for PD (LEAPD) for binary classification of 3-year mortality status and correlation between LEAPD indices and time to death. Methods2-minutes resting-state EEG from 94 PD patients (59 channels, 22 deceased within 3 years of recording) was used for binary classification of 3-year mortality status. Single-channel classification using a balanced dataset of 44 was performed using leave-one-out cross-validation (LOOCV). Robustness was evaluated by truncating the recordings. LOOCV Spearmans correlation coefficient ({rho}) was obtained between LEAPD indices and time to death. Optimum hyperparameters obtained from a balanced training dataset of 30 were tested on the remaining 64 patients by 10,000 randomized comparisons of 7 vs 7, using 5 channel combinations Hyperparameters for the best {rho}, using the same training dataset were for the out-of-sample correlation for the remaining 7 deceased. ResultsIn LOOCV analysis several channels yielded 100% accuracy with robust performance from five. The correlations ranged between {rho} =-0.59 to-0.86; were significant after adjusting for age, cognitive and motor impairment. Out-of-sample testing using the best-performing 5-channel combination yielded a mean accuracy of 83%. Out-of-sample Spearmans {rho} was-0.82. ConclusionLEAPD provides a robust approach for binary classification of mortality in PD from resting-state EEG. LEAPD indices correlate with survival duration, independent of clinical predictors, suggesting potential utility as a continuous neurophysiological biomarker.