A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension

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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, background): “(2017b) [62] • Tunable Q-wavelet transform SVM ACC: 98.6% Sharma et al. (2017) [63] • Analytic time- frequency flexible wavelet transform • Fractal dimension LS-SVM SEN: 100% This work • Ten-fold cross validation strategy • 13-layer deep CNN structure Convolutional neural network ACC: 88.7% SEN:…”
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, result): “A comparison with state-of-the-art methods is given in Table 8; Zhang-17 ((T. Zhang et al., 2017), Sharma-17 (Sharma et al., 2017), Swami-16 (Swami et al., 2016), Sharmila-16 (Sharmila et al., 2016), Samiee-15 (Samiee et al., 2015), Orhan-11 (Orhan et al, 2011), Tzallas-12 (Tzallas et al., 2012).”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “to detect epileptic seizures [10], [11], [12], [13], [14], [15].”