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• W2000064609 abstract "The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82%, specificity equal to 85.93%, accuracy equal to 84,05% and Cohen's kappa equal to 0.50." @default.
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• W2000064609 date "2011-08-01" @default.
• W2000064609 modified "2023-10-12" @default.
• W2000064609 title "Automatic detection of CAP on central and fronto-central EEG leads via support vector machines" @default.
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• W2000064609 doi "https://doi.org/10.1109/iembs.2011.6090364" @default.
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