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.
Automatic detection of CAP on central and fronto-central EEG leads via Support Vector Machines / S., Mariani; A., Grassi; M. O., Mendez; Parrino, Liborio; Terzano, Mario Giovanni; A. M., Bianchi. - In: IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE. - ISSN 0739-5175. - 2011:(2011), pp. 1491-1494. [10.1109/IEMBS.2011.6090364]
Automatic detection of CAP on central and fronto-central EEG leads via Support Vector Machines.
PARRINO, Liborio;TERZANO, Mario Giovanni;
2011-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
