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Updated: Jan 8, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

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

Simin Jamshidi¹, Arturo I Espinoza², Jonathan T Heinzman³

¹Department of Computer and Electrical Engineering, College of Engineering, University of Iowa, Iowa City, IA, USA.

Clinical Parkinsonism & Related Disorders

|December 24, 2025

Summary

This summary is machine-generated.

Predicting Parkinson's disease mortality is challenging. Short resting-state electroencephalography (EEG) with machine learning accurately forecasts 3-year survival in PD patients.

Keywords:

Linear Predictive Coding Machine Learning Mortality Prediction Parkinson’s Disease

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Last Updated: Jan 8, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Parkinson's disease (PD) mortality prediction is difficult due to patient heterogeneity and lack of reliable prognostic markers.
Increased mortality in PD necessitates improved prognostic tools.

Purpose of the Study:

To classify 3-year mortality status in PD patients using electroencephalography (EEG).
To correlate LEAPD (Linear Predictive Coding EEG Algorithm for PD) indices with time to death.

Main Methods:

Utilized 2-minute resting-state EEG recordings from 94 PD patients.
Employed the LEAPD algorithm for binary classification of 3-year mortality and correlation analysis.
Performed leave-one-out cross-validation (LOOCV) and out-of-sample testing for robustness and accuracy.

Main Results:

Several EEG channels achieved 100% LOOCV accuracy for mortality prediction.
Correlations between LEAPD indices and time to death ranged from ρ = -0.59 to -0.86, remaining significant after adjustments.
Out-of-sample testing demonstrated a mean accuracy of 83% with a Spearman's ρ of -0.82.

Conclusions:

Short resting-state EEG combined with machine learning algorithms like LEAPD can effectively predict mortality in Parkinson's disease.
This approach offers a promising, non-invasive tool for prognostic assessment in PD.