Authors: Velayutham Vimala; K. Ramar
Addresses: Department of Computer Science and Engineering, National Engineering College, K.R. Nagar, Thoothukudi District, Kovilpatti – 628503, India ' Department of Computer Science and Engineering, Einstein College of Engineering, Tirunelveli, Tamil Nadu, India
Abstract: Neonates are infants who are in their first 28 days of life. The diagnoses of neonatal seizures have been advocated by the use of clinical observations and electroencephalography (EEG). The continuous monitoring of neonatal EEGs in neonatal intensive care units is tedious and involves experts' intervention. The use of clinical decision support systems into the neonatal intensive care units has proved to produce aid to neonatal staff. The neonatal seizures of epileptic origin are more common and we recommend an approach to aid in the classification of the same using EEG signals of the neonates. Daubechies wavelet transform is used for the task of separation of frequency bands and the extraction of features. The theta rhythm of EEG reflects rightly the occurrence of epileptic seizures in neonates. The features taken into consideration for the classification are mean, variance, skewness and kurtosis. The support vector machine (SVM)-based classification is adopted for the development of the system which detects the presence or absence of epileptic seizures. The performance of this diagnostic aid system has been studied and the system has a sensitivity of 94% and specificity of 96%. The receiver operating characteristic curve is also used in the performance assessment.
Keywords: classification; electroencephalography; EEG; neonatal intensive care units; neonatal epileptic seizures; support vector machine; SVM.
International Journal of Advanced Intelligence Paradigms, 2019 Vol.12 No.1/2, pp.57 - 67
Received: 04 Aug 2016
Accepted: 04 Oct 2016
Published online: 26 Nov 2018 *