Analysis of EEG signal for seizure detection based on WPT
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inst Engineering Technology-Iet
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer-based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k-nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.
Açıklama
Anahtar Kelimeler
entropy, electroencephalography, wavelet transforms, signal classification, medical signal processing, feature extraction, medical signal detection, pattern classification, support vector machines, brain, electrical activities, automated system, computer-based expert opinion, objective view, EEG signal, wavelet packet, WPT, dispersion entropy, line length methods, extracted feature vector, support vector machine, SVM, neighbour classifiers, KNN, public EEG seizure dataset, binary cases, multiclass cases, evaluated classification accuracy, binary classification, multiclass classification evaluated accuracy, seizure detection, electroencephalography, diagnostic method, abnormally functioning part
Kaynak
Electronics Letters
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
56
Sayı
25
