Automatic detection of CAP on central and fronto-central EEG leads via support vector machines
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abstract
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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%25, specificity equal to 85.93%25, accuracy equal to 84,05%25 and Cohen%27s kappa equal to 0.50. © 2011 IEEE.
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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%25, specificity equal to 85.93%25, accuracy equal to 84,05%25 and Cohen's kappa equal to 0.50. © 2011 IEEE.
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Automatic Detection; Automatic detector; Cross-validation methods; Data sets; Descriptors; Healthy subjects; High-accuracy; Leave one out; Classification (of information); Support vector machines; adult; algorithm; article; automated pattern recognition; biological rhythm; brain; computer assisted diagnosis; electroencephalography; female; human; male; methodology; periodicity; physiology; reproducibility; sensitivity and specificity; support vector machine; Activity Cycles; Adult; Algorithms; Biological Clocks; Brain; Diagnosis, Computer-Assisted; Electroencephalography; Female; Humans; Male; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Support Vector Machines
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