Deep and Statistical Features Classification Model for Electroencephalography Signals
| dc.authorid | Karci, Ali/0000-0002-8489-8617 | |
| dc.authorwosid | Karci, Ali/AAG-5337-2019 | |
| dc.contributor.author | Karaduman, Mucahit | |
| dc.contributor.author | Karci, Ali | |
| dc.date.accessioned | 2024-08-04T20:53:31Z | |
| dc.date.available | 2024-08-04T20:53:31Z | |
| dc.date.issued | 2022 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description.abstract | People strive to make sense of the complex electroencephalography (EEG) data generated by the brain. This study uses a prepared dataset to examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals from each electrode are connected to one another and are first represented as a single signal. The signal is then denoised through variation mode decomposition (VMD) during the preprocessing stage. The statistical and deep feature extraction phases are the two subsequent phases. The crucial step in the suggested strategy is to classify data using a combination of these two unique qualities. Deep and statistical feature performance was evaluated independently. Then, using the eigenvectors created by merging all of the collected features, classification was carried out using our DSFC (Deep - Statistical Features Classification) model. Although the classification accuracy rate using only statistical features was 81.2 percent and the classification accuracy rate using only deep learning was 95.71 percent, the classification accuracy rate utilizing hybrid features created using the suggested DSFC technique was 99.2%. Therefore, it can be proven that combining statistical and deep features can produce beneficial results. | en_US |
| dc.description.sponsorship | Inonu University's Scientific Research Project Coordination Unit [FBA-20191664] | en_US |
| dc.description.sponsorship | The researchers would like to express their gratitude to Inonu University's Scientific Research Project Coordination Unit for their assistance with the funding project FBA-20191664. We thank Henri Begleiter at the Neurodynamics Laboratory at the State University of New York Health Center at Brooklyn for data. | en_US |
| dc.identifier.doi | 10.18280/ts.390508 | |
| dc.identifier.endpage | 1525 | en_US |
| dc.identifier.issn | 0765-0019 | |
| dc.identifier.issn | 1958-5608 | |
| dc.identifier.issue | 5 | en_US |
| dc.identifier.scopus | 2-s2.0-85150310451 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.startpage | 1517 | en_US |
| dc.identifier.uri | https://doi.org/10.18280/ts.390508 | |
| dc.identifier.uri | https://hdl.handle.net/11616/101229 | |
| dc.identifier.volume | 39 | en_US |
| dc.identifier.wos | WOS:000907630800047 | en_US |
| dc.identifier.wosquality | Q3 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Int Information & Engineering Technology Assoc | en_US |
| dc.relation.ispartof | Traitement Du Signal | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Electroencephalography (EEG) | en_US |
| dc.subject | alcohol use disorder (AUD) | en_US |
| dc.subject | support vector machine (SVM) | en_US |
| dc.subject | hybrid feature | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | Spectrogram | en_US |
| dc.subject | variation mode decomposition (VMD) | en_US |
| dc.title | Deep and Statistical Features Classification Model for Electroencephalography Signals | en_US |
| dc.type | Article | en_US |
