A training algorithm for optimal margin classifiers
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The nature of statistical learning theory
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Support vector machines: hype or hallelujah?
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A Tutorial on Support Vector Machines for Pattern Recognition
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Automatic seizure detection based on time-frequency analysis and artificial neural networks
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Discovering novelty in spatio/temporal data using one-class support vector machines
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Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm
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Signal regularity-based automated seizure detection system for scalp EEG monitoring
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Computational Intelligence and Neuroscience
Early detection of epileptic seizures based on parameter identification of neural mass model
Computers in Biology and Medicine
Diversity measures for one-class classifier ensembles
Neurocomputing
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Information Sciences: an International Journal
Review: A review of novelty detection
Signal Processing
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This paper describes an application of one-class support vector machine (SVM) novelty detection for detecting seizures in humans. Our technique maps intracranial electroencephalogram (EEG) time series into corresponding novelty sequences by classifying short-time, energy-based statistics computed from one-second windows of data. We train a classifier on epochs of interictal (normal) EEG. During ictal (seizure) epochs of EEG, seizure activity induces distributional changes in feature space that increase the empirical outlier fraction. A hypothesis test determines when the parameter change differs significantly from its nominal value, signaling a seizure detection event. Outputs are gated in a .one-shot. manner using persistence to reduce the false alarm rate of the system. The detector was validated using leave-one-out cross-validation (LOO-CV) on a sample of 41 interictal and 29 ictal epochs, and achieved 97.1% sensitivity, a mean detection latency of -7.58 seconds, and an asymptotic false positive rate (FPR) of 1.56 false positives per hour (Fp/hr). These results are better than those obtained from a novelty detection technique based on Mahalanobis distance outlier detection, and comparable to the performance of a supervised learning technique used in experimental implantable devices (Echauz et al., 2001). The novelty detection paradigm overcomes three significant limitations of competing methods: the need to collect seizure data, precisely mark seizure onset and offset times, and perform patient-specific parameter tuning for detector training.