An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals
- Title
- An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals
- Creator
- Aswathy S.U.; Vincent B.; Jacob P.M.; Aniyan N.; Daniel D.; Thomas J.
- Description
- Depression is a common and real clinical disease that has a negative impact on how you feel, how you think, and how you behave. It is a significant burdensome problem. Fortunately, it can also be treated. Feelings of self-pity and a lack of interest in activities you once enjoyed are symptoms of depression. It can cause a variety of serious problems that are real, and it can make it harder for you to work both at home and at work. The main causes include family history, illness, medications, and personality, all of which are linked to electroencephalogram (EEG) signals, which are thought of as the most reliable tools for diagnosing depression because they reflect the state of the human cerebrum's functioning. Deep learning (DL), which has been extensively used in this field, is one of the new emerging technologies that is revolutionizing it. In order to classify depression using EEG signals, this paper presents an efficient deep learning model that allows for the following steps: (a) acquisition of data from the psychiatry department at the Government Medical College in Kozhikode, Kerala, India, totaling 4200 files; (b) preprocessing of these raw EEG signals to avoid line noise without committing filtering; (c) feature extraction using Stacked Denoising Autoevolution; and (d) reference of the signal to estimate true and all. According to experimental findings, The proposed model outperforms other cutting-edge models in a number of ways (accuracy: 0.96, sensitivity: 0.97, specificity: 0.97, detection rate: 0.94). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Source
- Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 1179-1188.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Autoencoder; Classification; Convolutional Neural Network; Depression; Electroencephalogram
- Coverage
- Aswathy S.U., Department of Computer Science and Engineering, Marian Engineering College, Kerala, Thiruvananthapuram, India; Vincent B., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Jacob P.M., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Aniyan N., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Daniel D., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Thomas J., Department of Computer Science and Engineering, Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303127408-4
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Aswathy S.U.; Vincent B.; Jacob P.M.; Aniyan N.; Daniel D.; Thomas J., “An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 31, 2025, https://archives.christuniversity.in/items/show/19912.