Investigation of epileptic seizure signatures classification in EEG using supervised machine learning algorithms
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
International Information and Engineering Technology Association
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Epilepsy is one of the earnest neurological disorders that require further social attention.
Based on the International League Against Epilepsy (ILAE), which classifies the epilepsy
term as a number of several seizures that occur in the brain. Electroencephalography (EEG)
is considered our brain window to the electrical activity. It is a significant device used for
diagnosing multiple brain disorders such as Epilepsy. Moreover, this study used data from
Temple University Hospital Seizure Corpus (TUH), which represents an accurate
description of the clinical cases for five types of epileptic seizures. Initially, to extract
information from EEG signals, three types of feature extraction have been used namely Fast
Fourier Transform, Entropy, and Approximate Entropy. Due to the high degree of variance
of EEG signals, we implemented a band-pass filter to divide the signals into sub-bands called
delta rhythm (0.1 - 4Hz), theta rhythm (5 -9Hz), alpha rhythm (10 - 14Hz), beta rhythm (15-
31Hz), and gamma rhythm (32-100). The feature extraction outcome underwent
normalization techniques and was used as input for the classifiers. Support Vector Machine
(SVM), Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) classifier
have implemented in order to classify (1) second epoch length window. In the first scenario,
we applied the FFT features to the classifiers, the results showed that SVM obtained the
highest value compared to the other classifiers with 96% accuracy, whereas KNN was 92%
and the DT and NB were 76% and 67%, respectively. The second scenario was applying
entropy features to the classifiers, the results of classification were 91% for SVM and 88%
for KNN, while the DT and NB were 76% and 67%, respectively. The last scenario was
ApEn, which shows that SVM still gains the highest value, which was 83%, and 76% for
KNN, where the DT and NB were 65% and 69%, respectively. From the aforementioned
results, we deduced that SVM achieved the best accuracy when applied with the three feature
extractions.
Açıklama
Anahtar Kelimeler
Electroencephalogram (EEG), Fast Fourier Transform (FFT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification Epileptic Seizure
Kaynak
Traitement du Signal
WoS Q Değeri
Q4
Scopus Q Değeri
N/A
Cilt
40
Sayı
1
Künye
Al-jumaili, S., Duru, A. D., Ibrahim, A. A., Uçan, O. N. (2023). Investigation of epileptic seizure signatures classification in EEG using supervised machine learning algorithms. Traitement du Signal, 40(1), 43-54.
