Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks

By V. Srinivasan, Chikkannan Eswaran, Natarajan Sriraam. IEEE Transactions on Information Technology in Biomedicine, 2007

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Automated EEG analysis of epilepsy: A review (2013, methodology): “[104] ApEn Probabilistic neural network, Elman network 100”
EEG based multi-class seizure type classification using convolutional neural network and transfer learning (2020, neutral): “…seizure type (Acharya, et al., 2012; Acharya, Sree, Alvin, & Suri, 2012; Bogaarts, Gommer, Hilkman, van Kranen-Mastenbroek, & Reulen, 2016; Geng, Zhou, Zhang, & Geng, 2016; Raghu, Sriraam, Hegde, et al., 2019; Raghu et al., 2016; Raghu, Sriraam, Rao, et al., 2019; Srinivasan et al., 2007).”
Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, background): “A logistic regression model specifies that an appropriate function of the fitted probability of the event is a linear function of the observed values of the available explanatory variable.”
Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform (2015, neutral): “4) Approximate entropy-based feature extraction [37].”