title: Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals.
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.
authors.affiliation: Department of Electronic Engineering, Keimyung University, Daegu 704-701, Korea. bglee@kmu.ac.kr.
authors.forename: Boon-Giin
authors.initials: BG
authors.lastname: Lee
authors.affiliation: Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea. boringdragonlee@hotmail.com.
authors.forename: Boon-Leng
authors.initials: BL
authors.lastname: Lee
authors.affiliation: Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea. wychung@pknu.ac.kr.
authors.forename: Wan-Young
authors.initials: WY
authors.lastname: Chung
mesh_terms: D000063:Accidents, Traffic; D001334:Automobile Driving; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D012119:Respiration; D012894:Sleep Stages; D060388:Support Vector Machine; D058067:Wavelet Analysis