Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network
Jahanian Najafabadi A, Bagh K (2023) .
Preprint | Englisch
Download
Autor*in
Jahanian Najafabadi, AmirUniBi
;
Bagh, Khaled
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Abstract / Bemerkung
Previous studies have applied Deep learning models on Electroencephalography data for classification and prediction tasks. In this work, we aim to test a model using convolutional neural network (CNN) in order to identify biomarkers in childhood and Adolescents with major depression disorder from age matched healthy young individuals. CNN used to transfer learning from the VGG16 network. The data were tested using several filtered frequencies combined with a preprocessing pipeline. Overall, results achieved an accuracy of 0.875 and F1-Score of 0.638 and revealed that while lower Delta (0.856 accuracy and 0.539 F1-Score), and higher Theta activity (0.895 accuracy and 0.717 F1-Score), and higher Alpha (0.804 accuracy and 0.606 F1-Score) were classified in MDD compared with healthy. We did not find whether Beta and Gamma are biomarkers for MMD with higher accuracy. We further showed that while in MDD group, delta frequency bands were featured in left temporal, occipital, bilateral frontal and central regions; theta frequency bands were featured in left temporal and frontal, left occipital and central regions, and Alpha frequency band was featured in left frontal, central, left occipital and left temporal regions.
Erscheinungsjahr
2023
Urheberrecht / Lizenzen
Page URI
https://pub.uni-bielefeld.de/record/2980678
Zitieren
Jahanian Najafabadi A, Bagh K. Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network. 2023.
Jahanian Najafabadi, A., & Bagh, K. (2023). Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network. https://doi.org/10.31234/osf.io/8j9e6
Jahanian Najafabadi, Amir, and Bagh, Khaled. 2023. “Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network”.
Jahanian Najafabadi, A., and Bagh, K. (2023). Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network.
Jahanian Najafabadi, A., & Bagh, K., 2023. Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network.
A. Jahanian Najafabadi and K. Bagh, “Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network”, 2023.
Jahanian Najafabadi, A., Bagh, K.: Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network. (2023).
Jahanian Najafabadi, Amir, and Bagh, Khaled. “Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network”. (2023).
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Creative Commons Namensnennung 4.0 International Public License (CC-BY 4.0):
Volltext(e)
Name
Access Level
![Open access file OA](https://pub.uni-bielefeld.de/images/access_open.png)
Zuletzt Hochgeladen
2023-07-07T14:22:58Z
MD5 Prüfsumme
40e5aae0cc8780bcafcc6e3c7f3d4d34
Link(s) zu Volltext(en)
Access Level
![Open access file OA](https://pub.uni-bielefeld.de/images/access_open.png)