ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Translational Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1651762
This article is part of the Research TopicApplications of Intelligent Sensing and Biomedical Information Processing in Clinical NeuroscienceView all 3 articles
A Novel Fast Detection Algorithm for Depression Based on 3-Channel EEG Signals
Provisionally accepted
- 1Taihe People's Hospital, Fuyang, China
- 2Tianjin Tianshi College, Tianjin, China
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Medically unexplained symptoms (MUS) are an emerging field in current research. Among middle-aged and elderly patients, most MUS symptoms are mainly caused by depression, but early symptoms do not meet the international somatization standards, which delays treatment. Therefore, developing a rapid auxiliary diagnosis method is of great significance. This paper proposes a novel model for identifying depression based on 3-channel electroencephalogram (EEG) signals from the prefrontal lobe of the human brain. For the collected resting-state EEG signals, variational mode decomposition (VMD) is first used for signal decomposition, and the power spectrum is employed to select intrinsic mode function (IMF) components. After extracting energy features via sample entropy, LightGBM is adopted for classification, with a classification accuracy of 97.42%. Through comparative experiments, the model proposed in this paper achieves a balance between high accuracy and timeliness. This is conducive to the development of a depression detection system based on portable real-time electroencephalography (EEG), and provides a solution for EEG signal devices in real-time depression detection and pre-triage of patients with Medically Unexplained Symptoms (MUS).
Keywords: Medically unexplained symptoms, EEG signals, Depression, Lightgbm, VMD
Received: 22 Jun 2025; Accepted: 04 Sep 2025.
Copyright: © 2025 Guo, Guo and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: XiWu Guo, Taihe People's Hospital, Fuyang, China
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