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Damir Mulc¹, Jaksa Vukojevic¹, Eda Kalafatic²

¹University Psychiatric Hospital Vrapče, Bolnička Cesta 32, 10000 Zagreb, Croatia.

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|January 25, 2025

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Electroencephalography (EEG) combined with machine learning (ML) shows promise for diagnosing major depressive disorder (MDD). This study achieved 80% accuracy, suggesting EEG as a potential objective diagnostic aid for depression.

Area of Science:

Neuroscience
Computational Psychiatry
Medical Diagnostics

Background:

Major depressive disorder (MDD) diagnosis is challenging due to varied symptoms, leading to low diagnosis and treatment rates.
Objective diagnostic tools are needed to improve MDD detection and management.
Electroencephalography (EEG) offers a potential non-invasive method for assessing brain activity related to depression.

Purpose of the Study:

To evaluate the efficacy of EEG signals analyzed by machine learning (ML) models in identifying individuals with MDD.
To determine the accuracy of ML-based EEG analysis in differentiating MDD patients from healthy controls.

Main Methods:

Analysis of 140 EEG recordings from individuals diagnosed with MDD and healthy volunteers.
Application of various machine learning classification models to EEG data.

Keywords:

depression detection electroencephalography machine learning major depressive disorder

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Quantitative assessment of classification accuracy.

Main Results:

Machine learning models achieved up to 80% accuracy in distinguishing MDD patients from healthy controls.
EEG-based analysis demonstrated potential for objective depression identification.

Conclusions:

EEG combined with ML shows significant potential as a diagnostic aid for major depressive disorder.
Further research is needed to address clinical variability and patient-specific factors for successful integration into practice.