Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

By Hasan Ocak. Expert systems with applications, 2009

778 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8c59a872972c6a71938f094ae0e27682e165dae4

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Citations in the corpus

Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, methodology): “Ocak et al.(40) Approximate Entropy (1) Thresholding 96”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, background): “These paradigms range from directly controlling devices such as mouse cursors [7, 8], to passive brain monitoring for estimating states such as alertness or fatigue [9–11] or the occurrence of epileptic seizures [12–14], to paradigms for spelling [15] or image analysis [16].”
Automated EEG analysis of epilepsy: A review (2013, methodology): “Jahankhani et al. [52], Sadati et al. [99], Subasi [109] and Ocak [74] used DWT in their work for automated detection of epilepsy.”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “In addition, as a result in [Oca09], the ApEn had small values in a seizure group and using DWT could improve and provide some useful information for the AESD.”
EEG signals classification using the K-means clustering and a multilayer perceptron neural network model (2011, methodology): “Some basic statistics over the vectors are used to reduce the dimensionality of these vectors, such as average power, mean, entropy and standard deviation of the wavelet coefficients in each sub-band (Ocak, 2009; Subasi, 2007; Ubeyli, 2009a, 2009b).”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, neutral): “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”
A review of epileptic seizure detection using machine learning classifiers (2020, background): “Some other studies on seizure detection based on a single feature, i.e., entropy and its sub-types such as approximate entropy (AE) and sample entropy (SE), have also been done [45, 72, 73, 111].”