Feature analysis of functional MRI for discrimination between normal and epileptogenic brain

Novel approach for fetal heart rate classification introducing grammatical evolution

“…In future work, we will examine a fitness function that is not depended on the selection of the classification method and we will try to maximize the separability of the classes in feature space [55,56]. This approach can be combined with simpler classifiers reducing the potential overfitting problems that can occur when neural networks are involved.…”

Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data

“…Such human involvement counteracts the main purpose of the semi-automated approach and depending on the false-positive rate of the detection method might be as laborious as marking the actual true-positive events without running the pattern classification. On the other hand, we demonstrated via our prior work on interictal biomarker detection algorithms that—for at least our epilepsy brain signal data— one may gain higher pattern classification performance for features selected using an evolutionary computation method than for features selected by conventional or popular-in-literature methods for iEEG (Firpi et al, 2007; Smart et al, 2007; Smart et al, 2011) and for fMRI (Burrell et al, 2007a). …”

“…One approach to developing semi-automated pattern classification and decision-support tools for epilepsy data has been in implementing evolutionary computation techniques to select, combine, or create measures (extracted features) that quantify the difference between interictal biomarkers (e.g., pathological gamma oscillations in iEEG, resting-state blood oxygenation changes in fMRI) and interictal background or basal activity (Burrell et al, 2007a; Smart et al, 2007; Smart et al, 2011). Scant research has been published on the application of evolutionary computation to interictal resting-state fMRI signals from epilepsy patients (Burrell et al, 2007b).…”

“…Scant research has been published on the application of evolutionary computation to interictal resting-state fMRI signals from epilepsy patients (Burrell et al, 2007b). Instead, pattern detection applications for ictal (seizure) activity dominate prior research involving evolutionary computation techniques applied to iEEG and EEG, including mostly genetic algorithms (Consul-Pacareu; Haydari et al, 2011; Hsu and Yu, 2010; Ocak, 2008; Patnaik and Manyam, 2008; Rivero et al, 2013; Shen et al, 2013), genetic programming (Sotelo 2013a; Sotelo et al, 2013b), and harmony search optimization (Gandhi et al, 2012; Zainuddin et al, 2013), although some projects have focused on spike detection applications (Haydari et al, 2011; Kinnear et al, 1999; Marchesi et al, 1997a; Shen et al, 2013).…”

“…For pattern classification problems to quantitatively discriminate non-biomarker (e.g., basal or baseline activity) and biomarker (e.g., spikes, seizures, PGOs, abnormal CBF activations) brain signals from epilepsy patients, GP has been used for both noninvasive and invasive electroencephalography (Fernández-Blanco et al, 2013; Firpi et al, 2005b, c, 2006; Guo et al, 2011; Lopes, 2007; Marchesi et al, 1997b; Smart et al, 2007; Sotelo et al, 2013a; Sotelo et al, 2013b), MEG (Georgopoulos et al, 2009; Theofilatos et al, 2009), and fMRI (Burrell et al, 2007b). However, GP is not a feature selection algorithm.…”