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.

中文翻译：

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使用小波包变换检测睡意的自动分类方法是从单通道EEG信号中提取时域特征。

背景

在一些重要的工作中（例如车辆驾驶，起重机操作，采矿爆破等）检测到人的嗜睡是防止事故的保障措施之一。在几种睡意检测（DD）方法中，神经科学和计算机科学知识的组合具有更好的区分清醒和睡眠状态的能力。当前的大多数模型是使用多传感器脑电图（EEG）信号，多域特征，预定义特征选择算法来实现的。因此，人们对使用通用最佳功能以更高的精度检测嵌入式平台上的睡意的方法产生了极大的兴趣。

新方法

通过利用小波包变换（WPT）从考虑的通道脑电图中提取时域特征，提出了基于单通道脑电图的睡意检测（DD）模型。通过提出的新颖特征选择方法减小了特征向量的维数。

结果

关于免费可用的实时睡眠分析EEG和模拟虚拟驾驶驾驶员（SVDD）EEG的建议模型分别达到了94.45％和85.3％的精度。

与现有方法的比较

结果表明，与使用具有时域基于子带的特征和特征选择方法的生理数据集的最新技术相比，所提出的DD方法具有更高的精度。通过分析具有四个特征的5秒EEG信号来检测嗜睡的任务是对我以前使用具有66个特征的30秒EEG信号检测嗜睡的工作的改进。

结论

通过使用提出的特征选择算法为所有对象选择唯一的优化特征，从使用WPT收集的EEG时域子带中获得的时域特征实现了极高的准确率。