Research on Biomedical Engineering
Research on Biomedical Engineering
Original Article

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

Silveira, Tiago da; Kozakevicius, Alice de Jesus; Rodrigues, Cesar Ramos

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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.


Signal processing, Drowsiness detection, Wavelet transform, Best m-term approximation, Frequency bands, DB2 Wavelet.


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