Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367004" target="_blank" >RIV/68407700:21230/23:00367004 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00367004

Výsledek na webu

<a href="https://doi.org/10.1109/TNSRE.2023.3293051" target="_blank" >https://doi.org/10.1109/TNSRE.2023.3293051</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TNSRE.2023.3293051" target="_blank" >10.1109/TNSRE.2023.3293051</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions

Popis výsledku v původním jazyce

Major Depressive Disorder (MDD) – can be evaluated by advanced neurocomputing and traditional machine learning techniques. This study aims to develop an automatic system based on a Brain-Computer Interface (BCI) to classify and score depressive patients by specific frequency bands and electrodes. In this study, two Residual Neural Networks (ResNets) based on electroencephalogram (EEG) monitoring are presented for classifying depression (classifier) and for scoring depressive severity (regression). Significant frequency bands and specific brain regions are selected to improve the performance of the ResNets. The algorithm, which is estimated by 10-fold cross-validation, attained an average accuracy rate ranging from 0.371 to 0.571 and achieved average Root-Mean-Square Error (RMSE) from 7.25 to 8.41. After using the beta frequency band and 16 specific EEG channels, we obtained the best-classifying accuracy at 0.871 and the smallest RMSE at 2.80. It was discovered that signals extracted from the beta band are more distinctive in depression classification, and these selected channels tend to perform better on scoring depressive severity. Our study also uncovered the different brain architectural connections by relying on phase coherence analysis. Increased delta deactivation accompanied by strong beta activation is the main feature of depression when the depression symptom is becoming more severe. We can therefore conclude that the model developed here is acceptable for classifying depression and for scoring depressive severity. Our model can offer physicians a model that consists of topological dependency, quantified semantic depressive symptoms and clinical features by using EEG signals. These selected brain regions and significant beta frequency bands can improve the performance of the BCI system for detecting depression and scoring depressive severity.

Název v anglickém jazyce

Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions

Popis výsledku anglicky

Major Depressive Disorder (MDD) – can be evaluated by advanced neurocomputing and traditional machine learning techniques. This study aims to develop an automatic system based on a Brain-Computer Interface (BCI) to classify and score depressive patients by specific frequency bands and electrodes. In this study, two Residual Neural Networks (ResNets) based on electroencephalogram (EEG) monitoring are presented for classifying depression (classifier) and for scoring depressive severity (regression). Significant frequency bands and specific brain regions are selected to improve the performance of the ResNets. The algorithm, which is estimated by 10-fold cross-validation, attained an average accuracy rate ranging from 0.371 to 0.571 and achieved average Root-Mean-Square Error (RMSE) from 7.25 to 8.41. After using the beta frequency band and 16 specific EEG channels, we obtained the best-classifying accuracy at 0.871 and the smallest RMSE at 2.80. It was discovered that signals extracted from the beta band are more distinctive in depression classification, and these selected channels tend to perform better on scoring depressive severity. Our study also uncovered the different brain architectural connections by relying on phase coherence analysis. Increased delta deactivation accompanied by strong beta activation is the main feature of depression when the depression symptom is becoming more severe. We can therefore conclude that the model developed here is acceptable for classifying depression and for scoring depressive severity. Our model can offer physicians a model that consists of topological dependency, quantified semantic depressive symptoms and clinical features by using EEG signals. These selected brain regions and significant beta frequency bands can improve the performance of the BCI system for detecting depression and scoring depressive severity.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10600 - Biological sciences

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Neural Systems and Rehabilitation Engineering

ISSN

1534-4320

e-ISSN

1558-0210

Svazek periodika

31

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

2964-2973

Kód UT WoS článku

001037757900001

EID výsledku v databázi Scopus

2-s2.0-85164431559