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- W2476224637 abstract "In both medicine and technology, human brainwave recognition is a very much discussed topic because of its wide applications. These range from disease diagnostic to brain-computer interfaces used to control various devices. In this paper, we present a novel combined approach to epileptic spike recognition from recorded electroencephalograms. The highest accuracy obtained is 99.22 % when measured with the 10-fold cross-validation of a single patient’s data. This is achieved by combining a continuous wavelet transform to provide classification marks and classifiers to assign them. Results are also collected for the scenario of training the classifiers on one record of the patient and testing on another, taken at a different time. This provides an insight on the classifier’s ability to generalize for a given patient." @default.
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- W2476224637 date "2016-01-01" @default.
- W2476224637 modified "2023-09-27" @default.
- W2476224637 title "An EEG Classification Approach Based on Intrinsic Signal Properties and Wavelets" @default.
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- W2476224637 doi "https://doi.org/10.1007/978-3-319-33609-1_12" @default.
- W2476224637 hasPublicationYear "2016" @default.
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