Authors
Pranesh Krishnan1, Sazali Yaacob1, Annapoorni Pranesh Krishnan1, Mohamed
Rizon2, *, Chun Kit Ang2
1Intelligent Automotive Systems Research Cluster, Electrical Electronic
and Automation Section, Malaysian Spanish Institute Universiti Kuala Lumpur,
Kulim, Kedah 09000, Malaysia
2Faculty of Engineering, Technology and Build Environment, UCSI University,
No 1, Jalan Menara Gading, Taman Connaught, Kuala Lumpur 56000, Malaysia
*Corresponding author. Email: [email protected]
Corresponding Author
Mohamed Rizon
Received 6 November 2019, Accepted 16 April 2020, Available Online 11 September
2020.
DOI
https://doi.org/10.2991/jrnal.k.200909.001How to use a DOI?
Keywords
Drowsiness; polysomnography; band power; short-time Fourier transform
Abstract
Sleeping on the wheels due to drowsiness is one of the major causes of
death tolls all over the world. The objective of this research article
is to classify drowsiness with alertness based on the Electroencephalogram
(EEG) signals using spectral and band power features. A publicly available
ULg DROZY database used in this research. Algorithms are developed to extract
the five EEG channels from the raw multimodal signal. By using a higher-order
Butterworth low pass filter, the high-frequency components above 50 Hz
are removed. Another bandpass filter bank separates the raw signals into
eight sub-bands, namely delta, theta, low alpha, high alpha, low beta,
mid beta, high beta and gamma. During pre-processing step, the signals
are segmented into an equal number of frames. An overlap of 50% and a frame
duration of 2 s using a rectangular time windowing approach segments the
signal into frames. Then, the feature extraction algorithm extracts the
relative band power features based on the short-time Fourier transform
for each frame. The extracted feature sets are further normalized and labelled
as drowsy and alert and then combined to form the final dataset. K-fold
cross-validation method is used. The dataset is trained using K-Nearest
Neighbor algorithm (KNN) and support vector machine classifiers, and the
results are compared. The KNN classifier produces 96.1% (dataset 1) and
95.5% (dataset 2) classification accuracy.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license
(http://creativecommons.org/licenses/by-nc/4.0/).