Drowsiness Detection using Band Power and log Energy Entropy Features Based on EEG Signals
Pranesh Krishnan1, Sazali Yaacob2
1Pranesh Krishnan, Post-Doctoral Researcher, Intelligent Automotive Systems Research Cluster, Universiti Kuala Lumpur Malaysian Spanish Institute, 09000, Kulim, Kedah, Malaysia.
2Sazali Yaacob, Professor, Electrical Electronic and Automation Section, Universiti Kuala Lumpur Malaysian Spanish Institute, 09000, Kulim, Kedah, Malaysia.
Manuscript received on 03 July 2019 | Revised Manuscript received on 06 July 2019 | Manuscript published on 30 August 2019 | PP: 830-836 | Volume-8 Issue-10, August 2019 | Retrieval Number: J90250881019/19©BEIESP | DOI: 10.35940/ijitee.J9025.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sleeping on the wheels due to drowsiness is one of the significant causes of death tolls all over the world. The primary reason for the sleepiness is due to lack of sleep and irregular sleep patterns. Several methods such as unobtrusive sensors, vehicle dynamics and obtrusive physiology sensors are used to diagnose drowsiness in drivers. However, the unobtrusive sensors detect drowsiness in the later stage. Whereas the physiological methods use obtrusive sensors such as electro-ocular, electro-myo and electro-encephalograms produce high accuracy in the early detection of drowsiness, which makes them a preferable solution. The objective of this research article is to classify drowsiness with alertness based on the electroencephalographic (EEG) signals using band power and log energy entropy features. A publicly available ULg DROZY database used in this research. The raw multimodal signal is processed to extract the five EEG channels. A passband filter with the cut off frequencies of 0.1 Hz and 50 Hz attenuates the high-frequency components. Another bandpass filter bank is designed to slice the raw signals into eight sub-bands, namely delta, theta, low alpha, high alpha, low beta, mid-beta, high beta and gamma. The preprocessed signals are segmented into an equal number of frames with a frame duration of 2 seconds using a rectangular time windowing approach with an overlap of 50%. Frequency domain features such as log energy entropy and band power were extracted. The extracted feature sets were further normalised between 0 and 1 and labelled as drowsy and alert and then combined to form the final dataset. The K-fold cross-validation method is used to divide the dataset into training and testing sets. The processed dataset is then trained using Discriminant analysis, k-nearest neighbour network, Binary decision tree, ensemble, Naive Bayes and support vector machine classifiers and the results are compared with the literature. The kNN classifier produces 95% classification accuracy. The developed model can provide a tool for drowsiness detection in drivers.
Keywords: Band power, DROZY database, drowsiness, log energy entropy, polysomnography.
Scope of the Article: Renewable Energy Technology