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 proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models.

Conclusion: Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-022-00205-8.

Keywords: Deep learning; Depression; Electroencephalogram; Multi head attention; Transformer.

Fig. 1

a Architecture of the proposed attention-based GRU transformer network, b multi-head attention block, c architecture of adopted stacked GRU

Fig. 2

Workflow proposed for this research

Fig. 3

Steps followed for pre-processing the raw EEG signals

Fig. 4

Electrode positioning for 60 channels with respect to 10–20 electrode positioning system

Fig. 5

Graphical representation of accuracy obtained across different classifier models with and without feature selection (in percentage)

Fig. 6

ROC curves for all models used in the experiment without feature selection

Fig. 7

ROC curve for all models used in the experiment with boruta feature selection

Fig. 8

ROC curves for all models used in the experiment with RFE feature selection

Fig. 9

AttGRUT model accuracy and loss graph with RFE feature selection