Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals

Boon Giin Lee, Boon Leng Lee, Wan Young Chung

Research output: Journal PublicationArticlepeer-review

113 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)17915-17936
Number of pages22
JournalSensors
Volume14
Issue number10
DOIs
Publication statusPublished - 26 Sep 2014
Externally publishedYes

Keywords

  • Adaptive threshold filter
  • Electroencephalogram
  • Mobile healthcare
  • Mutual information
  • Respiration
  • Sleep onset
  • Support vector machine
  • Wavelet packet transform

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals'. Together they form a unique fingerprint.

Cite this