The Diagnosis Method of Major Depressive Disorder Using Wavelet Coherence and State-Pathology Separation Network

Major depressive disorder (MDD) is a serious psychiatric disorder characterized by persistent feelings of sadness, hopelessness, and lack of interest or pleasure in daily activities. Yet, reliable diagnostic tools for this brain disorder remain lacking. Functional near-infrared spectroscopy (fNIRS), an optical brain imaging technique, offers a promising approach for monitoring cerebral hemodynamic activity associated with MDD. In this study, we propose a novel algorithm based on wavelet coherence and a state-pathology separation network (WCSN) to automatically detect MDD using a dual-channel fNIRS system. The fNIRS signals were first preprocessed and transformed into two-dimensional feature maps using a wavelet coherence method. Following this, a wrapped exhaustive search was applied to select the optimal subset of feature maps, which was then utilized to reconstruct the dataset. Finally, samples were classified using the state-pathology separation network that employed a dual-encoder convolutional autoencoder (DCoAE) module to separate the feature maps into state features and pathology features, while a Transformer module distinguished MDD patients from healthy controls based solely on pathology features. The WCSN algorithm achieved exceptional performance with an accuracy of 0.923 ± 0.068 and a subject accuracy of 0.918 ± 0.076. Our result highlights the WCSN algorithm's ability to isolate pure pathology features, enhancing classification robustness and generalizability under dual-channel data conditions. Taken together, the proposed WCSN algorithm is well-suited for home-based MDD screening applications.