This research proposes the integration of a convolutional neural network (CNN) model with electroencephalography (EEG) for the detection of major depressive disorder (MDD). The proposed system utilizes EEG signal images with brain activity patterns associated with MDD, which are then processed by a CNN model trained to recognize characteristic EEG signatures of depression. The CNN model's output serves as an indicator of the presence and severity of MDD, facilitating early detection and intervention. This approach has the potential to revolutionize depression diagnosis by providing a more accessible, objective, and timely means of identifying individuals at risk of MDD, thereby improving patient outcomes and reducing the societal burden of this impending disorder.
Major depressive disorder (MOD}; electroencephalogram (EEG}; convolutional neural network (CNN); feature extraction; deep learning; neural network; depressive disorder
International Journal of Trend in Scientific Research and Development - IJTSRD having
online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International
Journal which provides rapid publication of your research articles and aims to promote
the theory and practice along with knowledge sharing between researchers, developers,
engineers, students, and practitioners working in and around the world in many areas
like Sciences, Technology, Innovation, Engineering, Agriculture, Management and
many more and it is recommended by all Universities, review articles and short communications
in all subjects. IJTSRD running an International Journal who are proving quality
publication of peer reviewed and refereed international journals from diverse fields
that emphasizes new research, development and their applications. IJTSRD provides
an online access to exchange your research work, technical notes & surveying results
among professionals throughout the world in e-journals. IJTSRD is a fastest growing
and dynamic professional organization. The aim of this organization is to provide
access not only to world class research resources, but through its professionals
aim to bring in a significant transformation in the real of open access journals
and online publishing.