ACNN-LSTM: A Novel Deep Learning Approach for Decoding Depression from Resting-State EEG

Abstract

Depression is a widespread psychological disorder that significantly impacts individuals. The conventional diagnostic approach through doctor-patient communication lacks objectivity and precision. The development of a more accurate and balanced technique for the detection of depression is critical. Due to its ability to effectively describe brain activity, resting electroencephalography (rEEG) is a valuable technique for investigating brain differences between healthy controls and patients with depression. This study examined the rEEG data from 95 healthy controls and 119 individuals with depression. This study constructed the brain's functional network and extracted linear spectral power density parameters from the rEEG data using correlation. Extracting distinctive features for identifying depression in rEEG data remains challenging due to the absence of crucial multichannel and temporal contexts in most existing methods. This research proposes taking advantage of an attention-based convolutional neural network with Long Short-Time Memory (ACNN-LSTM) to enhance its ability to identify of depressive disorders by extracting more selective features from rEEG signals. The ACNN-LSTM being presented incorporates a channel-wise attention mechanism to dynamically assign weights to different electrodes. A Convolutional Neural Network (CNN) is used to extract the important spatial details from the encoded rEEG data. An LSTM combines extended self-attention to examine the time-frequency trends in rEEG data by evaluating significance based on built-in similarities in the signals. The proposed model demonstrated strong performance with an accuracy of 97.34% in distinguishing between depression and healthy controls using 10-fold crossvalidation. According to our research, the front and occipital lobes-particularly the Fp1 and Fz locations and the O1 and O2 positions with the oscillations of beta and alpha-play the largest roles in the detection of depressive disorders.