Attention-based VGG-Residual-Inception Module for EEG-Based Depression Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149572" target="_blank" >RIV/00216305:26220/23:PU149572 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Attention-based VGG-Residual-Inception Module for EEG-Based Depression Detection

Popis výsledku v původním jazyce

Depression is a prevalent factor contributing to the increasing instances of suicide globally. Consequently, there is a pressing need for effective diagnosis and therapeutic interventions to alleviate depression symptoms. One potential tool for assessing depression levels is the electroencephalogram (EEG), a device that records and measures the brain’s electrical activity. Previous studies have demonstrated the potential of using EEG data and deep learning models to diagnose mental disorders, paving the way for better comprehension and treatment of depression. As a result, this study offers a novel attention-based visual geometry group-residual-inception module (A-VGGRI) for classifying EEG data from healthy and major depression disorder people. The Patient Health Questionnaire-9 score is utilized to measure the depression level in this case. A-VGGRI’s performance is examined using a depression dataset; the findings obtained by A-VGGRI have an accuracy of 96.35% and an area under the receiver operating characteristic curve of 0.96, demonstrating its usability in medical and industrial applications.

Název v anglickém jazyce

Attention-based VGG-Residual-Inception Module for EEG-Based Depression Detection

Popis výsledku anglicky

Depression is a prevalent factor contributing to the increasing instances of suicide globally. Consequently, there is a pressing need for effective diagnosis and therapeutic interventions to alleviate depression symptoms. One potential tool for assessing depression levels is the electroencephalogram (EEG), a device that records and measures the brain’s electrical activity. Previous studies have demonstrated the potential of using EEG data and deep learning models to diagnose mental disorders, paving the way for better comprehension and treatment of depression. As a result, this study offers a novel attention-based visual geometry group-residual-inception module (A-VGGRI) for classifying EEG data from healthy and major depression disorder people. The Patient Health Questionnaire-9 score is utilized to measure the depression level in this case. A-VGGRI’s performance is examined using a depression dataset; the findings obtained by A-VGGRI have an accuracy of 96.35% and an area under the receiver operating characteristic curve of 0.96, demonstrating its usability in medical and industrial applications.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/VJ02010019" target="_blank" >VJ02010019: Nástroje forenzní expertizy ručně psaného písma</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

33-37

Název nakladatele

Neuveden

Místo vydání

Ghent

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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