عنوان انگلیسی مقاله:
A deep learning framework for automatic diagnosis of unipolar depression
ترجمه فارسی عنوان مقاله:
یک چارچوب یادگیری عمیق برای تشخیص خودکار افسردگی تک قطبی
Sciencedirect - Elsevier - International Journal of Medical Informatics, 132 (2019) 103983: doi:10:1016/j:ijmedinf:2019:103983
Wajid Mumtaza,⁎, Abdul Qayyumb
Background and purpose: In recent years, the development of machine learning (ML) frameworks for automatic
diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea
needs further validation. Therefore, this paper has proposed an electroencephalographic (EEG)-based deep
learning framework that automatically discriminated depressed and healthy controls and provided the diagnosis.
Basic procedures: In this paper, two different deep learning architectures were proposed that utilized one dimensional
convolutional neural network (1DCNN) and 1DCNN with long short-term memory (LSTM) architecture.
The proposed deep learning architectures automatically learn patterns in the EEG data that were useful
for classifying the depressed and healthy controls. In addition, the proposed models were validated with restingstate
EEG data obtained from 33 depressed patients and 30 healthy controls.
Main findings: As results, significant differences were observed between the two groups. The classification results
involving the CNN model were accuracy=98.32%, precision=99.78%, recall=98.34%, and f-score=
97.65%. In addition, the study has reported LSTM with 1DCNN classification accuracy=95.97%, precision=
99.23%, recall=93.67%, and f-score=95.14%.
Conclusions: Deep learning frameworks could revolutionize the clinical applications for EEG-based diagnosis for
depression. Based on the results, it may be concluded that the deep learning framework could be used as an
automatic method for diagnosing the depression.
Keywords: EEG-based deep learning for depression | EEG-based diagnosis of unipolar depression | Convolutional neural network for depression | Long short-term memory classifiers for depression | EEG-based machine learning methods for depression