Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model.
Patients suffering from severe depression may be precisely assessed using online EEG categorization and their progress tracked over time, minimizing the risk of danger and suicide. Online EEG categorization systems, on the other hand, suffer additional challenges in the absence of empirical oversigh...
| Published in: | BioMed Research International pp. 1 - 10 |
|---|---|
| Main Authors: | , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
4/13/2022
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156288191&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156288191 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/13/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 156288191 156288191 156288191 10.1155/2022/5214195 156288191 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model. aug: au: Thakare, Anuradha Bhende, Manisha Deb, Nabamita Degadwala, Sheshang Pant, Bhasker Kumar, Yekula Prasanna affil: Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India sug: subj: Bioinformatics Electroencephalography Data Security Depression Physiopathology Brain Physiopathology Neural Networks (Computer) Human Deep Learning Artificial Intelligence Image Processing, Computer Assisted ab: Patients suffering from severe depression may be precisely assessed using online EEG categorization and their progress tracked over time, minimizing the risk of danger and suicide. Online EEG categorization systems, on the other hand, suffer additional challenges in the absence of empirical oversight. A lack of effective decoupling between brain regions and neural networks occurs during brain disease attacks, resulting in EEG data with poor signal intensity, high noise, and nonstationary characteristics. CNN employs momentum SGD optimization. By using a tiny momentum decay factor, the literature's starting strategy, and the same batch normalization, this work attempts to decrease model error. Before being utilized to form a training set, samples are shuffled, followed by validation and testing on the new samples in the set. An online EEG categorization system driven by a convolution neural network has been developed to do this. The approach is applied directly to the EEG input and is able to accurately and quickly identify depressed states without the need for preprocessing or feature extraction. The healthy control group and the depression control group had accuracy, sensitivity, and specificity of 99.08 percent, 98.77 percent, and 99.42 percent, respectively, in experiments on depression evaluation based on publicly accessible data. The machine learning technique based on feature extraction is often getting more and more complex, making it only suited for offline EEG categorization. While neural networks have become increasingly important in the study of artificial intelligence in recent years, they are still essentially black-box function approximations with limited interpretability. In addition, quantitative study of the neural network shows that depressed patients and healthy persons have remarkable dissimilarity between the right and left temporal lobe brain regions. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|