» Articles » PMID: 36979194

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Overview
Journal Brain Sci
Publisher MDPI
Date 2023 Mar 29
PMID 36979194
Authors
Affiliations
Soon will be listed here.
Abstract

Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms.

Citing Articles

Are neurasthenia and depression the same disease entity? An electroencephalography study.

Dang G, Zhu L, Lian C, Zeng S, Shi X, Pei Z BMC Psychiatry. 2025; 25(1):44.

PMID: 39825342 PMC: 11742223. DOI: 10.1186/s12888-025-06468-1.


Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach.

Luo X, Zhou B, Fang J, Cherif-Riahi Y, Li G, Shen X Diagnostics (Basel). 2024; 14(11).

PMID: 38893648 PMC: 11172130. DOI: 10.3390/diagnostics14111122.


Exploring Abnormal Brain Functional Connectivity in Healthy Adults, Depressive Disorder, and Generalized Anxiety Disorder through EEG Signals: A Machine Learning Approach for Triple Classification.

Fang J, Li G, Xu W, Liu W, Chen G, Zhu Y Brain Sci. 2024; 14(3).

PMID: 38539633 PMC: 10968909. DOI: 10.3390/brainsci14030245.


Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Xu Y, Zhong H, Ying S, Liu W, Chen G, Luo X Sensors (Basel). 2023; 23(20).

PMID: 37896732 PMC: 10611358. DOI: 10.3390/s23208639.


Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.

Qi X, Xu W, Li G Brain Sci. 2023; 13(9).

PMID: 37759883 PMC: 10526432. DOI: 10.3390/brainsci13091282.

References
1.
Mahato S, Goyal N, Ram D, Paul S . Detection of Depression and Scaling of Severity Using Six Channel EEG Data. J Med Syst. 2020; 44(7):118. DOI: 10.1007/s10916-020-01573-y. View

2.
Al-Ezzi A, Kamel N, Faye I, Gunaseli E . Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder. Front Psychol. 2020; 11:730. PMC: 7248208. DOI: 10.3389/fpsyg.2020.00730. View

3.
Ghiasi S, DellAcqua C, Benvenuti S, Scilingo E, Gentili C, Valenza G . Classifying subclinical depression using EEG spectral and connectivity measures. Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021:2050-2053. DOI: 10.1109/EMBC46164.2021.9630044. View

4.
Lu Q, Wang Y, Luo G, Li H, Yao Z . Dynamic connectivity laterality of the amygdala under negative stimulus in depression: a MEG study. Neurosci Lett. 2013; 547:42-7. DOI: 10.1016/j.neulet.2013.05.002. View

5.
Olbrich S, Trankner A, Chittka T, Hegerl U, Schonknecht P . Functional connectivity in major depression: increased phase synchronization between frontal cortical EEG-source estimates. Psychiatry Res. 2014; 222(1-2):91-9. DOI: 10.1016/j.pscychresns.2014.02.010. View