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Sensors 2014, 14(10), 17915-17936; doi:10.3390/s141017915

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

1
Department of Electronic Engineering, Keimyung University, Daegu 704-701, Korea
2
Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea
*
Author to whom correspondence should be addressed.
Received: 17 June 2014 / Revised: 17 September 2014 / Accepted: 19 September 2014 / Published: 26 September 2014
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
View Full-Text   |   Download PDF [4847 KB, uploaded 26 September 2014]   |  

Abstract

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. View Full-Text
Keywords: sleep onset; mobile healthcare; electroencephalogram; respiration; adaptive threshold filter; mutual information; wavelet packet transform; support vector machine sleep onset; mobile healthcare; electroencephalogram; respiration; adaptive threshold filter; mutual information; wavelet packet transform; support vector machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, B.-G.; Lee, B.-L.; Chung, W.-Y. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals. Sensors 2014, 14, 17915-17936.

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