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Open AccessArticle

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