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

Sensors

Boon-Giin Lee²  Boon-Leng Lee¹ 

[1] Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea; E-Mail:;Department of Electronic Engineering, Keimyung University, Daegu 704-701, Korea; E-Mail:

关键词: sleep onset;    mobile healthcare;    electroencephalogram;    respiration;    adaptive threshold filter;    mutual information;    wavelet packet transform;    support vector machine;   

DOI  :  10.3390/s141017915

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.