Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches.

ABSTRACT:

Significance

Early diagnosis of depression is crucial for effective treatment. Our study utilizes functional near-infrared spectroscopy (fNIRS) and machine learning to accurately classify mild and severe depression, providing an objective auxiliary diagnostic tool for mental health workers.

Aim

Develop prediction models to distinguish between severe and mild depression using fNIRS data.

Approach

We collected the fNIRS data from 140 subjects and applied a complete ensemble empirical mode decomposition with an adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove noise during the verbal fluency task. The temporal features (TF) and correlation features (CF) from 18 prefrontal lobe channels of subjects were extracted as predictors. Using recur

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11042567 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature