Resting-state EEG signal for major depressive disorder detection: A systematic validation on a large and diverse dataset

Chien Te Wu, Hao Chuan Huang, Shiuan Huang, I. Ming Chen, Shih Cheng Liao, Chih Ken Chen, Chemin Lin, Shwu Hua Lee, Mu Hong Chen, Chia Fen Tsai, Chang Hsin Weng, Li Wei Ko, Tzyy Ping Jung*, Yi Hung Liu*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

29 Scopus citations

Abstract

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the indepen-dent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Original languageEnglish
Article number499
JournalBiosensors
Volume11
Issue number12
DOIs
StatePublished - 12 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Electroencephalographic (EEG) signal
  • Feature selection
  • Healthcare
  • Machine learning
  • Major depressive disorder (MDD)
  • Support vector machine

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