Name
Title | ||
|---|---|---|
Robustness of time frequency distribution based features for automated neonatal EEG seizure detection. |
Abstract | ||
|---|---|---|
In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944212 | EMBC |
Keywords | DocType | Volume |
wigner distribution,paediatrics,medical disorders,modified b distribution,medical signal detection,electroencephalography,time-frequency distribution based features,seizure detection system,time-frequency image processing,smoothed wigner-ville distribution,tfd,automated neonatal eeg seizure detection,svm,support vector machine,time-frequency signal processing,neonatal seizure detection,support vector machines,time-frequency analysis | Conference | 2014 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S B Nagaraj | 1 | 0 | 0.34 |
| Nathan Stevenson | 2 | 45 | 6.56 |
| William P. Marnane | 3 | 427 | 41.38 |
| Geraldine B Boylan | 4 | 70 | 17.75 |
| Gordon Lightbody | 5 | 223 | 27.57 |