Name
Title | ||
|---|---|---|
Automatic detection of CAP on central and fronto-central EEG leads via Support Vector Machines. |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6090364 | EMBC |
Keywords | DocType | Volume |
medical signal detection,cyclic alternating pattern,electroencephalography,sleep,classification sensitivity,medical signal processing,leave one out cross-validation method,cohen kappa,automatic detection,feature extraction,signal classification,fronto-central eeg leads,support vector machines,leave one out cross validation,lead,support vector machine,kernel,accuracy,visualization | Conference | 2011 |
ISSN | ISBN | Citations |
1557-170X | 978-1-4244-4122-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Mariani | 1 | 22 | 4.00 |
| Andrea Grassi | 2 | 23 | 6.69 |
| Martin O Mendez | 3 | 123 | 19.21 |
| Liborio Parrino | 4 | 20 | 6.33 |
| Mario G Terzano | 5 | 0 | 0.34 |
| Anna M Bianchi | 6 | 41 | 16.77 |