Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.

ABSTRACT: Depression is a common mental disorder that seriously affects patients' social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven a

SUBMITTER: Yi L 

PROVIDER: S-EPMC10483285 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature