A unified approach for detection of induced epileptic seizures in rats using ECoG signals

Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines

“…In the literature, there are a wide range of available feature extraction methods which range from the traditional methods to the non-linear methods. Traditional methods include the fourier transform and also spectral analysis whilst the non-linear methods include Lyapunov exponents [28], [40], correlation dimension [28] and similarity [41]. After feature extraction has been implemented to the raw data, the extracted features are then used and applied to the pre-determined classification technique.…”

Automatic Seizure Detection Based on Nonlinear Dynamical Analysis of EEG Signals and Mutual Information

“…A series of studies have focused on Nonlinear Dynamical Analysis (NDA) of EEG signals to extract features for detection of epilepsy (Srinivasan, Eswaran, & Sriraam, 2007; Chen et al, 2011; Niknazar et al, 2013; Yaylali, Koçak, & Jayakar, 1996; Cerf, Amri, Ouasdad, & Hirsch, 1999; Adeli, Ghosh-Dastidar, & Dadmehr, 2007; Ghosh-Dastidar, Adeli, & Dadmehr, 2007; Iasemidis et al, 2003; Van Drongelen et al, 2003; Easwaramoorthy & Uthayakumar, 2011; Zhou, Liu, Yuan, & Li, 2013; Zabihi et al, 2016; Thomasson, Hoeppner, Webber, & Zbilut, 2001; Li, Ouyang, Yao, & Guan, 2004; Ouyang, Li, Dang, & Richards, 2008; Niknazar, Mousavi, Vahdat, & Sayyah 2013). These features include Approximate Entropy (ApEn) (Srinivasan et al, 2007; Chen et al, 2011; Niknazar et al, 2013), correlation dimension (Yaylali et al, 1996; Cerf et al, 1999; Adeli et al, 2007; Ghosh-Dastidar et al, 2007), Lyapunov exponent (Niknazar et al, 2013; Adeli et al, 2007; Ghosh-Dastidar et al, 2007; Iasemidis et al, 2003), Kolmogorov entropy (Van Drongelen et al, 2003), fractal dimension (Niknazar et al, 2013; Easwaramoorthy & Uthayakumar, 2011), lacunarity (Zhou et al, 2013), and features extracted from Poincaré section (Zabihi et al, 2016) as well as Recurrence Quantification Analysis (RQA) (Niknazar et al, 2013; Thomasson et al, 2001; Li et al, 2004; Ouyang et al, 2008; Niknazar et al, 2013).…”

“…These features include Approximate Entropy (ApEn) (Srinivasan et al, 2007; Chen et al, 2011; Niknazar et al, 2013), correlation dimension (Yaylali et al, 1996; Cerf et al, 1999; Adeli et al, 2007; Ghosh-Dastidar et al, 2007), Lyapunov exponent (Niknazar et al, 2013; Adeli et al, 2007; Ghosh-Dastidar et al, 2007; Iasemidis et al, 2003), Kolmogorov entropy (Van Drongelen et al, 2003), fractal dimension (Niknazar et al, 2013; Easwaramoorthy & Uthayakumar, 2011), lacunarity (Zhou et al, 2013), and features extracted from Poincaré section (Zabihi et al, 2016) as well as Recurrence Quantification Analysis (RQA) (Niknazar et al, 2013; Thomasson et al, 2001; Li et al, 2004; Ouyang et al, 2008; Niknazar et al, 2013). …”

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

“…The EEG has since become one of most useful tools for studying the cognitive processes and the physiology/pathology of the brain [8,9], especially the processes involved in epileptic seizures [10,11]. Currently, these methods mainly include traditional linear methods such as Fourier transforms and spectral analysis [12] and nonlinear methods such as Lyapunov exponents [13], correlation dimension [14] and similarity [15,16]. In particular, a series of entropy-based approaches has been widely used since they can quantify the complexity (regularity) of an EEG signal [17][18][19].…”