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

Background Parkinson’s disease (PD) increases mortality is difficult to predict because of its heterogeneity and the availability of very few reliable which prognostic markers.

We 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.

2-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 Spearman’s correlation coefficient (ρ) 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 ρ, using the same training dataset were for the out-of-sample correlation for the remaining 7 deceased.

In LOOCV analysis several channels yielded 100% accuracy with robust performance from five. The correlations ranged between ρ =-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 Spearman’s ρ was-0.82.

LEAPD 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was supported by the National Institutes of Health under grants R01NS100849-01A1 and 1RF1NS127809-01A1. The National Institute of Neurological Disorders and Stroke (NINDS) was not involved in the study's design, data collection, analysis, interpretation, or manuscript preparation. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the University of Iowa gave ethical approval for this work (IRB# 201707828). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Deidentified data and analysis code will be made publicly available upon publication via the Narayanan Lab and OpenNeuro.