Machine Learning Framework for Identification of Depression

Version 1.5.1 (156 MB) by Zhifei Li
Machine learning approaches (e.g. GA, SVM, KNN) are applied to detect MDD-related features from fNIRS signals.
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Updated 1 May 2022

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The proposed ML framework involved a sequence process of the fNIRS feature extraction, selection, classification, and validation.
The raw data that support the findings of this study are available on request from the corresponding author (pcmrhcm@nus.edu.sg). The data are not publicly available due to privacy or ethical restrictions.
Reference:
Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches
MATLAB Release Compatibility
Created with R2021b
Compatible with any release
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fnirs_fusion_feature_v1.5

fnirs_fusion_feature_v1.5/evaluation

fnirs_fusion_feature_v1.5/feature_set

fnirs_fusion_feature_v1.5/generate_feature

fnirs_fusion_feature_v1.5/select_feature

Version Published Release Notes
1.5.1

- Add reference
1.5

(1) Add 'nested_crossvalidation.m' to validate models with five-fold nested cross-validation.
(2) Add dataset 'samples_52ch_HbO.mat' for reproducing results.
(3) Update code comments.
1.0.3

Update Summary
1.0.2

add description on raw data.
1.0.1

add description
1.0.0