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

Zhiyong Huang; Man Liu; Hui Yang; Mengyao Wang; Yunlan Zhao; Xiao Han; Huan Chen; Yaju Feng

doi:10.1117/1.NPh.11.2.025001

24 April 2024 Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches

Zhiyong Huang, Man Liu, Hui Yang, Mengyao Wang, Yunlan Zhao, Xiao Han, Huan Chen, Yaju Feng

Author Affiliations +

Neurophotonics, Vol. 11, Issue 2, 025001 (April 2024). https://doi.org/10.1117/1.NPh.11.2.025001

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 recursive feature elimination with cross-validation, we identified optimal TF or CF and examined their role in distinguishing between severe and mild depression. Machine learning algorithms were used for classification.

Results

The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than using either TF or CF alone. Among the prediction models, the SVM-based model demonstrates excellent performance in nested cross-validation, achieving an accuracy rate of 92.8%.

Conclusions

The proposed model can effectively distinguish mild depression from severe depression.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Zhiyong Huang, Man Liu, Hui Yang, Mengyao Wang, Yunlan Zhao, Xiao Han, Huan Chen, and Yaju Feng "Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches," Neurophotonics 11(2), 025001 (24 April 2024). https://doi.org/10.1117/1.NPh.11.2.025001

Received: 12 September 2023; Accepted: 28 March 2024; Published: 24 April 2024

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KEYWORDS

Data modeling

Near infrared spectroscopy

Signal to noise ratio

Machine learning

Spectroscopy

Denoising

Neurophotonics

Significance

Aim

Approach

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