Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm

Hsu, Kai‐Cheng; Yu, Sung‐Nien

doi:10.1016/j.compbiomed.2010.08.005

Cited by 72 publications

(16 citation statements)

References 21 publications

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“…Support vector machine was proposed by Vapnik (1995), which maps training data samples into a higher dimensional space and seeks to discover a separating hyperplane with the maximal margin. SVM is becoming more and more popular in the development of automated epileptic seizure detection systems (Übeyli, 2008;Nicolaou and Georgiou, 2012;Hsu and Yu, 2010) due to its good generalization capability. However, the identification results with SVM tend to be very sensitive to the selection of kernel; in this study RBF kernel is chosen according to the recommendation from Hsu et al (2003).…”

Section: Performance Comparison Among Elm Svm and Bpnnmentioning

confidence: 99%

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Song

Crowcroft

Zhang

2012

Journal of Neuroscience Methods

218

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Section: Performance Comparison Among Elm Svm and Bpnnmentioning

confidence: 99%

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Song

Crowcroft

Zhang

2012

Journal of Neuroscience Methods

218

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“…According to the ANOVA test, no metabolites were statistically and significantly different among these three classes. To classify these three classes, we employed a machine-learning method with SVM and feature selection proposed in our previous work [40]. In the case study, the metabolic network comprised 34 metabolites.…”

Section: Resultsmentioning

confidence: 99%

Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity

Hsu

Wang

2017

PLoS ONE

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Precision medicine considers an individual’s unique physiological characteristics as strongly influential in disease vulnerability and in response to specific therapies. Predicting an individual’s susceptibility to developing an illness, making an accurate diagnosis, maximizing therapeutic effects, and minimizing adverse effects for treatment are essential in precision medicine. We introduced model-based precision medicine optimization approaches, including pathogenesis, biomarker detection, and drug target discovery, for treating presynaptic dopamine overactivity. Three classes of one-hit and two-hit enzyme defects were detected as the causes of disease states by the optimization approach of pathogenesis. The cluster analysis and support vector machine was used to detect optimal biomarkers in order to discriminate the accurate etiology from three classes of disease states. Finally, the fuzzy decision-making method was employed to discover common and specific drug targets for each classified disease state. We observed that more accurate diagnoses achieved higher satisfaction grades and dosed fewer enzyme targets to treat the disease. Furthermore, satisfaction grades for common drugs were lower than for specific ones, but common drugs could simultaneously treat several disease states that had different etiologies.

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“…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). Additional studies have used evolutionary computation in other manners for epilepsy data (Bandarabadi et al, 2011; Firpi et al, 2005a; Harikumar et al, 2004; Rivero et al, 2013; Wei et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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

Smart

Burrell

2015

Engineering Applications of Artificial Intelligence

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Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.

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Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm

Cited by 72 publications

References 21 publications

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity

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

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