Linear predictive coding electroencephalography algorithms predict mortality in Parkinson's disease.

ABSTRACT:

Background

Mortality is increased in Parkinson's disease (PD) and is difficult to predict because of its heterogeneity and the availability of few reliable prognostic markers.

Objectives

We used electroencephalography (EEG) recordings and the Linear Predictive Coding EEG Algorithm for PD (LEAPD) to classify 3-year mortality status and correlate LEAPD indices with time to death.

Methods

2-minutes resting-state EEG from 94 PD patients was used for binary classification of 3-year mortality status (22 deceased). 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

SUBMITTER: Jamshidi S 

PROVIDER: S-EPMC12719220 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature