Electroencephalogram-Based Major Depressive Disorder Classification Using Convolutional Neural Network and Transfer Learning

Şuheda Kaya; Burak Tasci

Makale detayları

Electroencephalogram-Based Major Depressive Disorder Classification Using Convolutional Neural Network and Transfer Learning

Yazarlar:

Şuheda Kaya | Burak Tasci

Yayıncı:

Fırat University

Dergi:

Turkish Journal of Science and Technology Vol.18 Issue 1, pp.207-214

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285854

pISSN:

1308-9080

Anahtar kelimeler:

Özet:

 Major Depressive Disorder MDD is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65 for Channel 1, 87.00 for Channel 2, and 86.94 for Channel 3.

 Major Depressive Disorder MDD is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65 for Channel 1, 87.00 for Channel 2, and 86.94 for Channel 3.

 Major Depressive Disorder MDD is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65 for Channel 1, 87.00 for Channel 2, and 86.94 for Channel 3.

 Major Depressive Disorder MDD is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65 for Channel 1, 87.00 for Channel 2, and 86.94 for Channel 3.

Makale detayları

Electroencephalogram-Based Major Depressive Disorder Classification Using Convolutional Neural Network and Transfer Learning

Yazarlar:

Şuheda Kaya | Burak Tasci

Yayıncı:

Fırat University

Dergi:

Turkish Journal of Science and Technology Vol.18 Issue 1, pp.207-214

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285854

pISSN:

1308-9080

Major Depressive Disorder

EEG

ReliefF

kNN

Major Depresif Bozukluk; EEG; ReliefF; k-EYK

Major Depressive Disorder;EEG; ReliefF; kNN

; EEG; ReliefF; kNN

Sayı

Makale detayları

Yazarlar: Şuheda Kaya | Burak Tasci

Yayıncı: Fırat University

Dergi: Turkish Journal of Science and Technology Vol.18 Issue 1, pp.207-214

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001285854

pISSN: 1308-9080

Sayı

Yazarlar:

Şuheda Kaya | Burak Tasci

Yayıncı:

Fırat University

Dergi:

Turkish Journal of Science and Technology Vol.18 Issue 1, pp.207-214

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285854

pISSN:

1308-9080