EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

ABSTRACT: In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI

SUBMITTER: Aljalal M 

PROVIDER: S-EPMC11312237 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature