Venkata Phanikrishna B1, Suchismitha Chinara2. 1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India. Electronic address: krishnasai.researcher@gmail.com. 2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India.
Abstract
BACKGROUND: Detecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features. NEW- METHOD: Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method. RESULTS: The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively. COMPARISON-WITH-EXISTING- METHOD: The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features. CONCLUSIONS: Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.
BACKGROUND: Detecting humandrowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features. NEW- METHOD: Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method. RESULTS: The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively. COMPARISON-WITH-EXISTING- METHOD: The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features. CONCLUSIONS: Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.
Authors: Fazla Rabbi Mashrur; Khandoker Mahmudur Rahman; Mohammad Tohidul Islam Miya; Ravi Vaidyanathan; Syed Ferhat Anwar; Farhana Sarker; Khondaker A Mamun Journal: Front Hum Neurosci Date: 2022-05-26 Impact factor: 3.473