EEG microstates, spectral analysis, and risk prediction in epilepsy comorbid with mild cognitive impairment: alteration in intrinsic brain activity.

ABSTRACT:

Objective

This study aims to investigate the differences in electroencephalogram (EEG) microstates and power spectrum between patients with epilepsy (PWE) comorbid with (without) mild cognitive impairment (MCI) and to develop a machine learning model to predict the risk of MCI comorbidity in PWE.

Method

Participants were classified into PWE comorbid with MCI (EPMCI) and PWE comorbid without MCI (EPNMCI). The microstate parameters and power spectral density (PSD) of both groups were compared. We combined different types of variables and constructed models using Support Vector Machine (SVM), Neural Network (NNET), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). An ideal predictive model was selected to evaluate the risk of MCI comorbidity in PWE.

Result

SUBMITTER: Fang S 

PROVIDER: S-EPMC12487139 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature