Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals
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 clas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/10/17915 |
| _version_ | 1797999549508222976 |
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| author | Boon-Giin Lee Boon-Leng Lee Wan-Young Chung |
| author_facet | Boon-Giin Lee Boon-Leng Lee Wan-Young Chung |
| author_sort | Boon-Giin Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:05:13Z |
| format | Article |
| id | doaj.art-726e9a137b55468d9965e07ca28acde6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:05:13Z |
| publishDate | 2014-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-726e9a137b55468d9965e07ca28acde62022-12-22T04:28:21ZengMDPI AGSensors1424-82202014-09-011410179151793610.3390/s141017915s141017915Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration SignalsBoon-Giin Lee0Boon-Leng Lee1Wan-Young Chung2Department of Electronic Engineering, Keimyung University, Daegu 704-701, KoreaDepartment of Electronic Engineering, Pukyong National University, Busan 608-737, KoreaDepartment of Electronic Engineering, Pukyong National University, Busan 608-737, KoreaDriving 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.http://www.mdpi.com/1424-8220/14/10/17915sleep onsetmobile healthcareelectroencephalogramrespirationadaptive threshold filtermutual informationwavelet packet transformsupport vector machine |
| spellingShingle | Boon-Giin Lee Boon-Leng Lee Wan-Young Chung Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals Sensors sleep onset mobile healthcare electroencephalogram respiration adaptive threshold filter mutual information wavelet packet transform support vector machine |
| title | Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals |
| title_full | Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals |
| title_fullStr | Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals |
| title_full_unstemmed | Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals |
| title_short | Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals |
| title_sort | mobile healthcare for automatic driving sleep onset detection using wavelet based eeg and respiration signals |
| topic | sleep onset mobile healthcare electroencephalogram respiration adaptive threshold filter mutual information wavelet packet transform support vector machine |
| url | http://www.mdpi.com/1424-8220/14/10/17915 |
| work_keys_str_mv | AT boongiinlee mobilehealthcareforautomaticdrivingsleeponsetdetectionusingwaveletbasedeegandrespirationsignals AT boonlenglee mobilehealthcareforautomaticdrivingsleeponsetdetectionusingwaveletbasedeegandrespirationsignals AT wanyoungchung mobilehealthcareforautomaticdrivingsleeponsetdetectionusingwaveletbasedeegandrespirationsignals |