An Algorithm for Automatic Detection of Drowsiness for Use in
Wearable EEG Systems
Wearable EEG Systems
File(s)
Accepted version
Author(s)
Patrick, KCA
Imtiaz, SA
Bowyer, S
Rodriguez Villegas, E
Type
Conference Paper
Abstract
Lack of proper restorative sleep can induce sleepiness
at odd hours making a person drowsy. This onset of drowsiness
can be detrimental for the individual in a number of ways
if it happens at an unwanted time. For example, drowsiness
while driving a vehicle or operating heavy machinery poses a
threat to the safety and wellbeing of individuals as well as those
around them. Timely detection of drowsiness can prevent the
occurrence of unfortunate accidents thereby improving road
and work environment safety. In this paper, by analyzing the
electroencephalographic (EEG) signals of human subjects in
the frequency domain, several features across different EEG
channels are explored. Of these, three features are identified to
have a strong correlation with drowsiness. A weighted sum of
these defining features, extracted from a single EEG channel,
is then used with a simple classifier to automatically separate
the state of wakefulness from drowsiness. The proposed algorithm
resulted in drowsiness detection sensitivity of 85% and
specificity of 93%.
at odd hours making a person drowsy. This onset of drowsiness
can be detrimental for the individual in a number of ways
if it happens at an unwanted time. For example, drowsiness
while driving a vehicle or operating heavy machinery poses a
threat to the safety and wellbeing of individuals as well as those
around them. Timely detection of drowsiness can prevent the
occurrence of unfortunate accidents thereby improving road
and work environment safety. In this paper, by analyzing the
electroencephalographic (EEG) signals of human subjects in
the frequency domain, several features across different EEG
channels are explored. Of these, three features are identified to
have a strong correlation with drowsiness. A weighted sum of
these defining features, extracted from a single EEG channel,
is then used with a simple classifier to automatically separate
the state of wakefulness from drowsiness. The proposed algorithm
resulted in drowsiness detection sensitivity of 85% and
specificity of 93%.
Date Issued
2016-10-18
Date Acceptance
2016-06-23
Citation
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2016
ISSN
1557-170X
Publisher
IEEE
Journal / Book Title
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Grant Number
679417
Source
IEEE EMBC 2016
Subjects
Science & Technology
Technology
Engineering, Biomedical
Engineering, Electrical & Electronic
Engineering
Algorithms
Databases, Factual
Electroencephalography
Humans
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Sleep
Sleep Stages
Wakefulness
Publication Status
Published
Start Date
2016-08-16
Finish Date
2016-08-20
Coverage Spatial
Orlando, Florida