Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.

ABSTRACT:

Purpose

Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression.

Methods

An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features.

Results

The

SUBMITTER: Tigga NP 

PROVIDER: S-EPMC9800680 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature