Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression

• Published in: Computers in biology and medicine Vol. 145; p. 105420
• Main Authors: Soni, Surbhi, Seal, Ayan, Yazidi, Anis, Krejcar, Ondrej
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2022; Elsevier Limited
• Subjects: Accuracy; Algorithms; Asymmetry; Brain research; Brain-Computer Interfaces; Channels; Classification; Datasets; Decision-level fusion; Depression - diagnosis; Depressive Disorder, Major - diagnosis; EEG; Electroencephalography; Embedding; Euclidean geometry; Feature extraction; Feature-level fusion; Fractals; Graph representations; Graph-level fusion; Graphical representations; Humans; Internal Medicine; Machine learning; Magnetic resonance imaging; Mental depression; Mental disorders; Neural networks; Node2vec; Nodes; Other; Signal classification; Signal processing; Wavelet transforms; Decision-level fusion; Electroencephalography; Feature-level fusion; Graph-level fusion; Node2vec
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105420

Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods. •It aims to fetch facets from EEG signals using graph representation learning.•Graph representation learning is done using Node2vec in three levels of fusion.•ML algorithms take these features to classify the depressed and healthy person.