Ensemble approach for detection of depression using EEG features

TL;DR

This study explores the use of EEG features combined with various classifiers to detect depression, achieving up to 90% accuracy, aiming to aid early diagnosis and understanding of depression's long-term effects.

Contribution

It introduces an ensemble approach using multiple classifiers and feature sets to improve depression detection from EEG signals, with comprehensive accuracy evaluation.

Findings

Several classifier and feature combinations reached 90% accuracy.

Linear and non-linear EEG features are effective for depression detection.

The approach demonstrates potential for non-invasive depression diagnosis.

Abstract

Depression is a public health issue which severely affects one's well being and cause negative social and economic effect for society. To rise awareness of these problems, this publication aims to determine if long lasting effects of depression can be determined from electoencephalographic (EEG) signals. The article contains accuracy comparison for SVM, LDA, NB, kNN and D3 binary classifiers which were trained using linear (relative band powers, APV, SASI) and non-linear (HFD, LZC, DFA) EEG features. The age and gender matched dataset consisted of 10 healthy subjects and 10 subjects with depression diagnosis at some point in their lifetime. Several of the proposed feature selection and classifier combinations reached accuracy of 90% where all models where evaluated using 10-fold cross validation and averaged over 100 repetitions with random sample permutations.