Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

• Published in: Signal, image and video processing Vol. 8; no. 7; pp. 1323 - 1334
• Main Authors: Kumar, Yatindra, Dewal, M. L., Anand, R. S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2014
• Subjects: Computer Imaging; Computer Science; Image Processing and Computer Vision; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; Approximate entropy (ApEn); Support vector machine (SVM); Artificial neural network (ANN); Electroencephalogram (EEG); Discrete wavelet transforms(DWT)
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-012-0362-9

There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.