Drowsiness detection for single channel EEG by DWT best m-term approximation

• Autor(a) principal: Silveira,Tiago da
• Data de Publicação: 2015
• Outros Autores: Kozakevicius,Alice de Jesus, Rodrigues,Cesar Ramos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research on Biomedical Engineering (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402015000200107
• topic: Signal processing; Drowsiness detection; Wavelet transform; Best m-term approximation; Frequency bands; DB2 Wavelet

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.