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

Tiago da Silveira, Alice de Jesus Kozakevicius, Cesar Ramos Rodrigues
2015 Research on Biomedical Engineering  
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
more » ... 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.
doi:10.1590/2446-4740.0693 fatcat:5aqz56amt5gyhayzdpeacifymq