Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.
Please read the tutorial at this link: https://ebookbell.com/faq
We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.
For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.
EbookBell Team
4.3
68 reviewsAbstract— Depression is a common reason for an increase in
suicide cases worldwide. Thus, to mitigate the effects of depres-
sion, accurate diagnosis and treatment are needed. An electroen-
cephalogram (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.
Index Terms— Convolutional neural network (CNN), electroen-
cephalography, measurement of depression, pattern classification,
visualization.
