Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

ABSTRACT:

Objective

We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for PD (LEAPD). This method efficiently encodes EEG time series into features that can detect PD in a computationally fast manner amenable to real time applications.

Methods

We included a total of 41 PD patients and 41 demographically-matched controls from New Mexico and Iowa. Data for all participants from New Mexico (27 PD patients and 27 controls) were used to evaluate in-sample LEAPD performance, with extensive cross-validation. Participants from Iowa (14 PD patients and 14 controls) were used for out-of-sample tests. Our method utilized data from six EEG leads which were as little as 2 min long.

Results<

SUBMITTER: Anjum MF 

PROVIDER: S-EPMC7900258 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature