Drowsiness detection for single channel EEG by DWT best m-term approximation
Res. Biomed. Eng. (Online)
; 31(2): 107-115, Apr-Jun/2015. tab, graf
Article
em En
| LILACS
| ID: biblio-829428
Biblioteca responsável:
BR1.1
ABSTRACT
Introduction In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG) signal preprocessed through a discrete wavelet transform. Methods In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR) of 84.98% and 98.65% of precision (PPV). Conclusion The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
LILACS
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
Res. Biomed. Eng. (Online)
Assunto da revista:
Engenharia Biomdica
Ano de publicação:
2015
Tipo de documento:
Article
/
Project document
País de afiliação:
Brasil
País de publicação:
Brasil