Robustness of time frequency distribution based features for automated neonatal EEG seizure detection.
Annu Int Conf IEEE Eng Med Biol Soc
; 2014: 2829-32, 2014.
Article
em En
| MEDLINE
| ID: mdl-25570580
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Convulsões
/
Eletroencefalografia
/
Doenças do Recém-Nascido
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
/
Newborn
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article