A deep learning framework for automatic diagnosis of unipolar depression. (December 2019)

• Record Type: Journal Article
• Authors: Mumtaz, Wajid; Qayyum, Abdul
• Is Part Of: International journal of medical informatics. Volume 132(2019)
• Publication Date: 2019-12
• Subjects: 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; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.103983
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: A deep learning framework for automatic diagnosis of unipolar depression is presented. The models are tested and validated with real-world EEG data. The models advocate the notion of using EEG as a modality for diagnosing depression. Abstract: 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 resting-state 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.