GNN-Based Depression Recognition Using Spatio-Temporal Information: A fNIRS Study

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 10; pp. 4925 - 4935
• Main Authors: Yu, Qiao, Wang, Rui, Liu, Jia, Hu, Long, Chen, Min, Liu, Zhongchun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; Depression; Depression Recognition; Electroencephalography; Feature extraction; fNIRS; Functional Connectivity; Functional near-infrared spectroscopy; GNN; Graph neural networks; Imaging techniques; Infrared spectra; Infrared spectroscopy; Machine learning; Medical imaging; Medical personnel; Motivation; Near infrared radiation; Neural networks; Neuroimaging; Spatial analysis; Spatial data; Spatial databases; Spatio-temporal Feature; Task analysis; Temporal variations
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3195066

In recent years, depression has become an increasingly serious problem globally. Previous studies of automatic depression recognition based on functional near-Infrared spectroscopy (fNIRS) or other brain imaging techniques have shown potential to serve as auxiliary diagnosis methods that provide assistance to medical professionals. Recently, some studies have found that, besides directly using the data themselves (temporal data), the use of functional connectivity among channels (spatial data) also can be effective. In this paper, we propose a method based on Graph Neural Network (GNN) that combines both temporal and spatial features of fNIRS data for automatic depression recognition. Specifically, fNIRS data of 96 subjects were collected and pre-processed. Basic statistical metrics of each channel were extracted as temporal features, and channel connectivity (coherence and correlation) were calculated as spatial features. Point-biserial analysis was conducted on these features and depression labels as a data-driven motivation. For classification, we considered data of each subject as a graph, with temporal features as node features and spatial features as edge weights. The graphs were fed into GNNs for training and testing. Experimental results showed that our GNN-based methods realized the best depression recognition performance compared with classical machine-learning methods regarding accuracy, F1 score, and precision, especially in F1 score for over 10%.