Selection of optimum frequency bands for detection of epileptiform patterns.
Healthc Technol Lett
; 6(5): 126-131, 2019 Oct.
Article
em En
| MEDLINE
| ID: mdl-31839968
ABSTRACT
The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.
automated seizure detection system; classification performance; classification process; electroencephalography; electrophysiological recordings; epileptiform patterns; frequency bins; medical disorders; medical signal detection; medical signal processing; neurophysiology; optimum frequency bands; scalp electroencephalograms; seizure classification rates; seizure episodes; signal classification; signal decomposition; state-of-the-art classification accuracy
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article