DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG
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- 25 January 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 70 (00189456), 1-13
- https://doi.org/10.1109/tim.2021.3053999
Abstract
Depression is a common reason for an increase in suicide cases worldwide. Thus, to mitigate the effects of depression, accurate diagnosis and treatment are needed. An electroencephalogram (EEG) is an instrument used to measure and record the brain’s electrical activities. It can be utilized to produce the exact report on the level of depression. Previous studies proved the feasibility of the usage of EEG data and deep learning (DL) models for diagnosing mental illness. Therefore, this study proposes a DL-based convolutional neural network (CNN) called DeprNet for classifying the EEG data of depressed and normal subjects. Here, the Patient Health Questionnaire 9 score is used for quantifying the level of depression. The performance of DeprNet in two experiments, namely, the recordwise split and the subjectwise split, is presented in this study. The results attained by DeprNet have an accuracy of 0.9937, and the area under the receiver operating characteristic curve (AUC) of 0.999 is achieved when recordwise split data are considered. On the other hand, an accuracy of 0.914 and the AUC of 0.956 are obtained, while subjectwise split data are employed. These results suggest that CNN trained on recordwise split data gets overtrained on EEG data with a small number of subjects. The performance of DeprNet is remarkable compared with the other eight baseline models. Furthermore, on visualizing the last CNN layer, it is found that the values of right electrodes are prominent for depressed subjects, whereas, for normal subjects, the values of left electrodes are prominent.Keywords
Funding Information
- Computer Science and Engineering
- PDPM Indian Institute of Information Technology
- Design and Manufacturing Jabalpur India (SPARC-MHRD-231)
- University of Hradec Kralove
- Faculty of Informatics and Management
- Universiti Teknologi Malaysia (Vot-20H04)
- Malaysia Research University Network MRUN (Vot 4L876)
- Fundamental Research Grant Scheme FRGS (Vot5F073)
- Ministry of Education Malaysia for the completion of the research
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