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Sirimonpak Suwannakhun. A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
Name: Balaji, Lakshmi R. S.
Organization :
Academy of Maritime Education and Training University (AMET). Department of Advanced Computing Sciences
Email :
rslbalaji@protonmail.com
Name: Batumalay, M.
Organization :
INTI International University. Faculty of Data Science and Information Technology
Email :
malathy.batumalay@newinti.edu.my
Name: Thaweesak Yingthawornsuk
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
thaweesak.yin@kmutt.ac.th
Name: Avanthika Swamy
Organization :
Birmingham City Council. Compliance Administrator
Email :
avni.1210_@protonmail.com
Name: Sirimonpak Suwannakhun
Organization :
King Mongkut's University of Technology Thonburi. Faculty of Industrial Education and Technology
Email :
sirimonpak.sut@kmutt.ac.th
LCSH:
Electroencephalography
LCSH:
Stress (Psychology)
LCSH:
Mental illness
Abstract:
The identification of psychological stress and
disorders like panic attacks and anxiety is vital for improving
mental health care. This study investigates the use of
electroencephalogram (EEG) signals to predict these conditions,
leveraging three deep learning models: a feedforward neural
network, a transformer-based model, and a hybrid
convolutional neural network (CNN)-long short-term memory
network (LSTM). Performance metrics, including accuracy, F1
score, precision, recall, RMSE, and R-squared, were used for
evaluation. The feedforward model achieved accuracies of
95.42% and 95.66% for predicting panic and anxiety attacks,
respectively, while the transformer-based model also delivered
promising results. Notably, the CNN-LSTM hybrid model
outperformed the others, achieving 95.90% accuracy for anxiety
prediction and excelling in stress level estimation with an RMSE
of 0.1671 and an R-squared value of 0.9438. These findings
demonstrate the hybrid model's efficacy in predicting
psychological conditions and estimating stress levels. This
approach holds significant promise for improving mental health
assessments, early interventions, and treatment strategies.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2025-06-24
Issued:
2025-06-24
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and King Mongkut's University of Technology North Bangkok. Department of Electrical and Computer Engineering. 13th International Electrical Engineering Congress (iEECON 2025) (P06428). Bangkok : Electrical Engineering Academic Association (Thailand), 2025
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.024865 วินาที
Balaji, Lakshmi R. S.
| Title | Contributor | Type |
|---|---|---|
| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, Lakshmi R. S.;Batumalay, M.;Thaweesak Yingthawornsuk;Avanthika Swamy;Sirimonpak Suwannakhun | บทความ/Article |
Batumalay, M.
| Title | Contributor | Type |
|---|---|---|
| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, Lakshmi R. S.;Batumalay, M.;Thaweesak Yingthawornsuk;Avanthika Swamy;Sirimonpak Suwannakhun | บทความ/Article |
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| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
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Avanthika Swamy
| Title | Contributor | Type |
|---|---|---|
| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, Lakshmi R. S.;Batumalay, M.;Thaweesak Yingthawornsuk;Avanthika Swamy;Sirimonpak Suwannakhun | บทความ/Article |
Sirimonpak Suwannakhun
| Title | Contributor | Type |
|---|---|---|
| Deep learning-based predictive modeling for male depression detection
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| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, Lakshmi R. S.;Batumalay, M.;Thaweesak Yingthawornsuk;Avanthika Swamy;Sirimonpak Suwannakhun | บทความ/Article |