Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks.

By Ling Guo, Daniel Rivero, Alejandro Pazos. Journal of Neuroscience Methods, 2010

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Automated EEG analysis of epilepsy: A review (2013, methodology): “2% using wavelet transform and ApEn features [41], an accuracy of 99.”
Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, background): “It is widely used in many areas of biomedical signal processing, such as EEG epileptic activity analysis [46], background activity [47], coronary artery disease (CAD) heart rate signal analysis [48], etc.”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “The results indicated that the ANN was the most optimal classiﬁer, and the absolute median and some absolute coeﬃcients were the most dominant features.”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, positive): “The following features are used for the two class classification of epilepsy: Approximate Entropy (ApEn) on DWT coefficients [40,41], relative wavelet energy [42], line length feature [43], Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA”