Deep and Statistical Features Classification Model for Electroencephalography Signals
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Int Information & Engineering Technology Assoc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Electroencephalography (EEG), alcohol use disorder (AUD), support vector machine (SVM), hybrid feature, deep learning, Spectrogram, variation mode decomposition (VMD)
Kaynak
Traitement Du Signal
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
39
Sayı
5
