Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.
Depression is one of the most prevalent mental disorders globally, severely affecting individuals' emotional, cognitive, and physical functions while imposing profound socioeconomic impacts. Traditional diagnostic approaches primarily rely on clinical judgment and self-assessment scales; however, these methods carry inherent risks of misdiagnosis and missed diagnosis, necessitating more precise and efficient diagnostic tools.
This study employs a two-channel frontal EEG system for depression detection, aiming to simplify data acquisition processes and reduce costs while ensuring high classification accuracy.
Electroencephalography (EEG), as a non-invasive biosignal monitoring technique, enables real-time recording of brain electrical activity. By extracting multiple features including relative power, fuzzy entropy, and mutual information, combined with multi-scale analysis techniques, the detection accuracy for depression was further enhanced.
The study compared three traditional machine learning models with three deep learning models, among which the Gated Recurrent Unit (GRU) model demonstrated superior performance, achieving a classification accuracy of 91.02% and exhibiting strong robustness.
The aforementioned approach provides preliminary technical support for the application of EEG signals in depression detection, and represents a proof-of-concept for multi-scale feature-enhanced automated depression screening. Further validation in larger, clinically representative, and externally verified cohorts is necessary before practical deployment can be considered.
This study employs a two-channel frontal EEG system for depression detection, aiming to simplify data acquisition processes and reduce costs while ensuring high classification accuracy.
Electroencephalography (EEG), as a non-invasive biosignal monitoring technique, enables real-time recording of brain electrical activity. By extracting multiple features including relative power, fuzzy entropy, and mutual information, combined with multi-scale analysis techniques, the detection accuracy for depression was further enhanced.
The study compared three traditional machine learning models with three deep learning models, among which the Gated Recurrent Unit (GRU) model demonstrated superior performance, achieving a classification accuracy of 91.02% and exhibiting strong robustness.
The aforementioned approach provides preliminary technical support for the application of EEG signals in depression detection, and represents a proof-of-concept for multi-scale feature-enhanced automated depression screening. Further validation in larger, clinically representative, and externally verified cohorts is necessary before practical deployment can be considered.