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Computation 2019, 7(1), 13;

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

STMicroelectronics, Stradale Primosole 50, 95121 Catania, Italy
Department of Biomedical and Biotechnological Sciences, Physiology Section, University of Catania, Via S. Sofia, 97, 95123 Catania, Italy
Authors to whom correspondence should be addressed.
Received: 12 January 2019 / Revised: 18 February 2019 / Accepted: 21 February 2019 / Published: 28 February 2019
(This article belongs to the Section Computational Engineering)
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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. View Full-Text
Keywords: EEG; DCT; Deep Learning EEG; DCT; Deep Learning

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Rundo, F.; Rinella, S.; Massimino, S.; Coco, M.; Fallica, G.; Parenti, R.; Conoci, S.; Perciavalle, V. An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal. Computation 2019, 7, 13.

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