Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders

Bibliographic Data

ID15523322
AuthorsXiaodong Luo (0000-0002-0734-862X, Jinhua Academy of Agricultural Sciences), Yan Xu (0000-0002-2441-8618, Zhejiang Normal University), Yanting Xu (0009-0008-5439-1947), Zihao Yan (0009-0005-9447-4913, Zhejiang Normal University), Wei Liu (0000-0002-5949-0302), W K Liu (0009-0003-8458-1948, Zhejiang Normal University), Bin Zhou (0000-0002-5816-445X, Zhejiang Normal University), Jinlong Shi (0000-0001-7308-2391), G Y Li (Zhejiang Normal University, corresponding author), Yixia Zhu (0000-0001-6342-5266, Jinhua Academy of Agricultural Sciences, corresponding author)
Year2026
Volume16
Pages1624997-1624997
Publication date2026-01-12
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2025.1624997
PMID41601517
OpenAlexW7122678244
LanguageEN
References cited77

Depressive disorder (DD), a leading global cause of disability, lacks objective diagnostic biomarkers due to reliance on subjective clinical criteria. This study introduces an algorithm-driven framework integrating multidimensional EEG features, dynamic time-window optimization, feature selection and machine learning to address this gap. Resting-state EEG signals were acquired from 70 DD patients and 30 healthy controls (HC). Three-dimensional neurophysiological features, including power spectral density (PSD), sample entropy (SE), and phase lag index (PLI), were systematically extracted across variable time windows. The SVM-RFE algorithm eliminated redundant features, identifying an optimal subset that maximized classification accuracy through leave-one-subject-out cross-validation. Our model achieved exceptional classification accuracy of 94.48% using 10-second windows, outperforming conventional approaches. Critical biomarkers included beta rhythm alterations and cross-frequency functional connectivity patterns, demonstrating superior discriminative power for DD patients. The optimal feature subset emphasized the combined significance of spectral, nonlinear dynamic, and network-level characteristics in differentiating DD from HC. This framework establishes the first evidence-based integration of time-window and feature selection optimized multidimensional EEG features for DD identification, resolving key limitations in replicability and clinical translatability of existing methods. Beyond enabling high-precision objective diagnosis, the biomarker profile provides mechanistic insights into DD neuropathology, particularly beta rhythm dysregulation and aberrant cross-frequency coupling. These findings advance EEG-based precision psychiatry by offering a validated protocol for therapeutic monitoring and treatment personalization, bridging the critical gap between computational neuroscience and clinical practice in mood disorder management

Discriminative model · Electroencephalography · Entropy (arrow of time · Feature (linguistics · Feature selection · Medical diagnosis · Neurophysiology · Pattern recognition (psychology · EEG and Brain-Computer Interfaces · Emotion and Mood Recognition · Functional Brain Connectivity Studies

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