Abstract
The detection of epileptic EEG signals is a challenging task due to bulky size and nonstationary nature of the data. From a pattern recognition point of view, one key problem is how to represent the large amount of recorded EEG signals for further analysis such as classification.This chapter introduces a new classification algorithm combining a simple random sampling (SRS) technique and a least square support vector machine (LS-SVM) to identify epilptic seizure from two-class EEG signals.
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Siuly, S., Li, Y., Zhang, Y. (2016). Random Sampling in the Detection of Epileptic EEG Signals. In: EEG Signal Analysis and Classification. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-47653-7_4
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DOI: https://doi.org/10.1007/978-3-319-47653-7_4
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