Human seizure detection using quadratic Rényi entropy
Document Type
Conference Proceeding
Date of Original Version
12-1-2013
Abstract
In this study, the quadratic Rényi entropy is applied for seizure detection from human electroencephalography (EEG) signals. Quadratic Rényi entropy was combined with two different methods; the empirical mode decomposition (EMD) and discrete wavelet transform (DWT). The use of these two methods is justified since EEGs are non-linear and non-stationary signals. First, the EEG signal is decomposed into sub-signals using the EMD method or the DWT. Then, the quadratic Rényi entropy is used as an input feature. The k-nearest neighbor (k-NN) classifier algorithm extracted the features with 99.5%-100% accuracy. © 2013 IEEE.
Publication Title, e.g., Journal
International IEEE EMBS Conference on Neural Engineering Ner
Citation/Publisher Attribution
Feltane, Amal, G. F.Boudreaux Bartels, John Gaitanis, Yacine Boudria, and Walter Besio. "Human seizure detection using quadratic Rényi entropy." International IEEE EMBS Conference on Neural Engineering Ner (2013). doi: 10.1109/NER.2013.6696059.