EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search

Abstract

Objective. Major Depressive Disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings’ mental activities and can be served as an objective biomarker for diagnosing MDD. Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels. Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition. Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.

Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder

“…Additionally, the top three features belong to the alpha band power. The alpha band power achieves a high classi cation accuracy, which is consistent with a previous study [41]. Finally, our results showed that feature set 1 alone achieves the highest classi cation accuracy, which was higher than that of the combination of two feature sets.…”

“…Pizzagalli et al showed that depressive subjects exhibited more β3 in the pronounced right inferior and superior frontal regions than healthy control subjects and less β3 in the posteromedial cluster, including the posterior cingulate cortex and precuneus cortex [3]. Arikan et al found that the group with suicidal ideation showed signi cantly higher high-gamma power (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) than other groups through rest-state EEG [4]. A recent study recruiting 44 late-life depression (LLD) patients and 41 healthy controls (HCs) showed that LLD patients had higher beta frequency activity and increased alpha activity than the HC group, and there were no correlations between beta power and the severity of MDD [5].…”

EEG-based high-performance depression state recognition

“…Therefore, the next study can start from each node to get the most representative feature information. Zong et al (2023) used the random search method to select the discriminative features for each channel. Maybe we can receive inspiration from it.…”

Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study

“…Recent studies have shown that integrating multiple neuroimaging modalities can improve the performance in the diagnosis of mental disorders. For example, Zheng et al [15] designed a Functional and Structural Co-attention Fusion (FSCF) module to explore potential associations between deep features from different modalities for MDD diagnosis, achieving an accuracy of 75.2%. Yuan et al [16] developed a Brain Dynamic Attention Network (BDANet) to dynamically generate sample-specific brain graphs using fMRI and sMRI images for identifying depression.…”