One-Class Novelty Detection for Seizure Analysis from Intracranial EEG

By Andrew B. Gardner, Abba M. Krieger, George J. Vachtsevanos, Brian Litt. Journal of machine learning research, 2006

315 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9fb8a2911f3d93c9f7ae4468990df944463e80ed

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Citations in the corpus

EEG-based neonatal seizure detection with Support Vector Machines (2011, unknown): “A one-class SVM methodology has also been used for seizure detection from intracranial EEG in adults (Gardner et al., 2006).”
Adversarially Learned One-Class Classifier for Novelty Detection (2018, background): “Novelty detection can be portrayed in the context of one-class classiﬁcation [30, 13, 18], which aims The ﬁrst row of (b) shows some example images, and the second row contains the output of the R network on them, i.e ., R ( X ) .”
Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks (2007, methodology): “…features can then be used to classify the EEG signal using statistical methods [28–30], nearest neighbour classiﬁers [36], decision trees [16], ANNs [21, 34], support vector machines (SVMs) [18, 37], or adaptive neuro-fuzzy inference systems [23, 35] in order to identify the occurrence of seizures.”
One-class classification: taxonomy of study and review of techniques (2013, background): “…Analysis (Sachs et al., 2006; Nguyen et al., 2011); Disease Detection (Cohen et al., 2004; Zhang et al., 2011); Medical Analysis (Zhou et al., 2005; Gardner et al., 2006); Bioinformatics (Spinosa & Ferreira de Carvalho, 2004; Ferreira de Carvalho, 2005; Alashwal et al., 2006; Wang et al., 2006;…”
Asymptotic normality of plug-in level set estimates. (2009, background): “2003) and EEG-based seizure analysis (Gardner et al. 2006).”
Application of Machine Learning To Epileptic Seizure Detection (2010, methodology): “In (Gardner et al., 2006), seizures of a single type were used (temporal lobe seizures); data was hand-selected by an expert to be free of artifacts; and only 29 seizure and 41 nonseizure epochs each of 15-minute duration were tested (for a total of 17 hours of test data that included 29 seizures).”
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, background): “Recently, a lot of research work have been carried out to detect the epileptic and non-epileptic signals as a classification problem (Gardner et al., 2006; Meier et al., 2008; Mirowski et al., 2009; Sheb et al., 2010).”
A Survey of Recent Trends in One Class Classification (2009, methodology): “OSVMs have been successfully applied in a wide variety of application domains such as Handwritten Digit Recognition [1][2][19], Information Retrieval [20], Face Recognition Applications [28][37], Medical Analysis [38], Bioinformatics [39][40], Spam Detection [41], AnomalyDetection [42][43], Machine ”