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About DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG
<p>Abstract— Depression is a common reason for an increase in</p><p>suicide cases worldwide. Thus, to mitigate the effects of depres-</p><p>sion, accurate diagnosis and treatment are needed. An electroen-</p><p>cephalogram (EEG) is an instrument used to measure and record</p><p>the brain’s electrical activities. It can be utilized to produce the</p><p>exact report on the level of depression. Previous studies proved</p><p>the feasibility of the usage of EEG data and deep learning (DL)</p><p>models for diagnosing mental illness. Therefore, this study</p><p>proposes a DL-based convolutional neural network (CNN) called</p><p>DeprNet for classifying the EEG data of depressed and normal</p><p>subjects. Here, the Patient Health Questionnaire 9 score is used</p><p>for quantifying the level of depression. The performance of</p><p>DeprNet in two experiments, namely, the recordwise split and the</p><p>subjectwise split, is presented in this study. The results attained</p><p>by DeprNet have an accuracy of 0.9937, and the area under</p><p>the receiver operating characteristic curve (AUC) of 0.999 is</p><p>achieved when recordwise split data are considered. On the other hand, an accuracy of 0.914 and the AUC of 0.956 are obtained,</p><p>while subjectwise split data are employed. These results suggest</p><p>that CNN trained on recordwise split data gets overtrained on</p><p>EEG data with a small number of subjects. The performance of</p><p>DeprNet is remarkable compared with the other eight baseline</p><p>models. Furthermore, on visualizing the last CNN layer, it is</p><p>found that the values of right electrodes are prominent for</p><p>depressed subjects, whereas, for normal subjects, the values of</p><p>left electrodes are prominent.</p><p>Index Terms— Convolutional neural network (CNN), electroen-</p><p>cephalography, measurement of depression, pattern classification,</p><p>visualization.</p>
Detailed Information
| Author: | Ayan Seal , Senior Member, IEEE, Rishabh Bajpai , Jagriti Agnihotri , Anis Yazidi , Senior Member, IEEE, Enrique Herrera-Viedma , Fellow, IEEE, and Ondrej Krejcar |
|---|---|
| Publication Year: | 2021 |
| Pages: | 10 |
| Language: | other |
| File Size: | 6.0908 |
| Format: | |
| Price: | FREE |
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