Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals

By Rajendra Acharya, Alwin Chuan, Jasjit S. Suri. International Journal of Neural Systems, 2012

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Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, unknown): “7% was reported using DWT [24] and a classification accuracy of 96% was reported using CWT [28].”
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques (2015, background): “Time domain features mainly include sample entropy [12], approximate entropy [12], permutation entropy, fractal dimension, Hjorth parameters [13], Hurst component [10], and Lyapunov exponent [14].”
Automated EEG analysis of epilepsy: A review (2013, methodology): “In a recent study by this group [5], nonlinear features based on HOS, two entropies, namely ApEn and SampEn, FD and H were extracted from the EEG segments of 6 s duration.”
Review on Psychological Stress Detection Using Biosignals (2019, background): “The majority of studies support the notion that in stress state there is generally greater frontal right alpha activity in relation to the left alpha activity [26].”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “Further, it is reported in Acharya et al. [46] that feeding entropy, HOS, and nonlinear features into fuzzy sugeno classifier achieved an accuracy of 99.7% and a sensitivity and specificity of 100%.”