Deep Learning in Computer Aided Diagnosis of MDD
Anna Dominic1, Aswathy K. J2, Surekha Mariam Varghese3
1Anna Dominic, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India.
2Dr. Surekha Mariam Varghese, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India.
3Aswathy K J, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 April 2019 | PP: 464-468 | Volume-8 Issue-6S April 2019 | Retrieval Number: F60950486S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Electroencephalogram (EEG) is a popular method for diagnosing various neurological diseases. Major Depressive Disorder (MDD) is a mental health disorder that can be diagnosed and treated by making use of EEG. One of the main challenges in using EEG to accurately identify depression is complexity and variation that exist in the EEG of a depressed person. Manually reading EEG and diagnosing depression is very challenging. An efficient computer aided method can be used for this task. Of the many methods that exists, a deep neural network method called Convolutional Neural Networks (CNN) proved to be the most efficient. In this paper a multi-layer deep CNN algorithm is implemented to diagnose depression from EEG of patients. Depression is classified based on a severity index into mild, moderate and major classes. The accuracy, sensitivity and specificity were measured by varying various parameters of the proposed algorithm.
Keywords: Convolutional Neural Network, Deep Learning, EEG, MDD.
Scope of the Article: Deep Learning