Abstract:
Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. The ...Show MoreMetadata
Abstract:
Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. The output of the classifiers were regularized by two methods: one based on the Kalman Filter (KF) and other based on a measure called the “Firing Power” (FP). The FP is a quantification of the rate of the classification in the preictal class in a past time window. In order to enable the application of the KF, the classification problem was subdivided in a two two-class problem, and the real-valued output of SVMs was considered. The results point that the FP method raise less false alarms than the KF approach. However, the KF approach presents an higher sensitivity, but the high number of false alarms turns their applicability negligible in some situations.
Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Date of Conference: 28 August 2012 - 01 September 2012
Date Added to IEEE Xplore: 10 November 2012
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ISSN Information:
PubMed ID: 23367425