Name
Title | ||
|---|---|---|
Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity |
Abstract | ||
|---|---|---|
We explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CBMS.2011.5999109 | Computer-Based Medical Systems |
Keywords | Field | DocType |
Bayes methods,brain,decision trees,electroencephalography,feature extraction,image segmentation,medical image processing,Bayesian assessment,adaptive EEG segmentation,decision trees,electroencephalograms,feature extraction,newborn brain maturity,pseudo stationary intervals | Decision tree,Histogram,Bayesian inference,Pattern recognition,Computer science,Segmentation,Speech recognition,Image segmentation,Feature extraction,Artificial intelligence,Electroencephalography,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1063-7125 | 978-1-4577-1189-3 | 2 |
PageRank | References | Authors |
0.40 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jakaite, L. | 1 | 2 | 0.40 |
| Vitaly Schetinin | 2 | 87 | 16.95 |
| Schult, J. | 3 | 2 | 0.40 |