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May 14, 2026CNS SpectrumsOpen Access

Machine learning-based differentiation of major depressive disorder and bipolar disorder using entropy-derived EEG biomarkers in drug-naïve patients

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Authors

HHHyeon-Ho HwangKCKang-Min ChoiHLHyeon-Ah Lee

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Overview

Randomized trial identifies EEG biomarkers to distinguish major depressive disorder from bipolar disorder, suggesting improved diagnosis accuracy.

Key Points

  • This research aims to differentiate major depressive disorder from bipolar disorder using EEG-derived entropy markers in drug-naïve patients.
  • Resting-state EEG collected from 92 drug-naïve patients (50 MDD, 16 converted BD, 26 diagnosed BD).
  • Bandscale entropy and cross-sample entropy were analyzed to assess group differences and classification performance.
  • Analysis of covariance was used to test group differences adjusted for depressive severity, and classification was evaluated with nested cross-validation.
  • Diagnosed BD exhibited higher theta and alpha-band entropy compared to stable MDD, with significant group differences in EEG measures.
  • Discrimination between stable MDD and converted BD showed modest performance (balanced accuracy of 65.4%, ROC-AUC of 0.661).
  • The strongest classification was for stable MDD versus diagnosed BD with pooled accuracy of 78.9% and ROC-AUC of 0.832 when combining entropy features with depressive severity.

Cite This Study

Hwang et al. (2026) studied this question.

synapsesocial.com/papers/6a0565f4a550a87e60a1e0eehttps://doi.org/10.1017/s1092852926100923
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Comprehensive Assessment of EEG Entropy Measures for Bipolar Disorder Diagnosis Using Machine Learning2025 · 6 citations
  2. 2Endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder: a prospective cohort study2024
  3. 3Microstate analysis from resting state electroencephalography in patients with bipolar disorder converted from major depressive disorder2026
  4. 4EEG complexity analysis using enhanced entropy features for depression detection and severity classification2026
  5. 5Integrated Chemometric and Machine Learning Analysis Identifies Peripheral Biosignatures Distinguishing Major Depressive Disorder from Bipolar Disorder: A Translational Cross-Sectional Study2026