The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

Rundo, Francesco;Rinella, Sergio;Massimino, Simona;Coco, Marinella;Parenti, Rosalba
;
Perciavalle, Vincenzo
2019

Abstract

The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.
EEG; DCT; Deep Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/362003
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