An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

Francesco Rundo,Simona Massimino,Giorgio Fallica,Vincenzo Perciavalle,Marinella Coco,Sabrina Conoci,Rosalba Parenti,Sergio Rinella

doi:10.3390/computation7010013

Francesco Rundo, Simona Massimino + Show 6 more

Open Access

https://doi.org/10.3390/computation7010013

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Journal: Computation	Publication Date: Feb 28, 2019
Citations: 50	License type: CC BY 4.0

Affiliation: STMicroelectronics (Italy), University of Catania

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.

Highlights

Drowsiness represents the transition state between awakening and sleep during which the vigilance decrement is observed
The most reliable methods for detecting drowsiness are those based on physiological measurements, including electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG)
EEG is the most widely used technique to measure the electrical activity of the brain, and since it is the standard technique in sleep studies, it has been proposed by several authors for drowsiness tracking analysis [3,4,5,6]

Summary

Introduction

Drowsiness represents the transition state between awakening and sleep during which the vigilance decrement is observed. The tracking of drowsiness represents the goal of increasing security in favor of human health This topic is becoming very important in healthcare applications for further improvement of medical assessment [1,2]. In this context, the most reliable methods for detecting drowsiness are those based on physiological measurements, including electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG). EEG has been extensively used for fatigue classification, which is strictly correlated to the drowsiness monitoring [7,8,9,10,11,12] In this context, appropriated EEG artifacts removal procedures must be implemented for obtaining reliable post-processed signals [13,14,15]

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