A study of morphology-based wavelet features and multiple-wavelet strategy for EEG signal classification: results and selected statistical analysis.
Annu Int Conf IEEE Eng Med Biol Soc
; 2013: 5998-6002, 2013.
Article
em En
| MEDLINE
| ID: mdl-24111106
ABSTRACT
Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
/
Eletroencefalografia
/
Análise de Ondaletas
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article