PDF(Pubmed)
Abstract：
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.
摘要：
困倦是各种代价高昂的缺陷的主要因素，甚至在建筑等领域的致命事故，交通运输,工业和医学，由于上述地区缺乏监测警惕。困倦检测系统的实施可以通过在个体进入困倦状态时警告个体来极大地帮助减少缺陷和事故率。这项研究提出了一种基于脑电图（EEG）的睡意检测方法。EEG信号通过由伪影去除和分割组成的预处理链，以确保准确检测，然后通过不同的特征提取方法来提取与困倦相关的不同特征。这项工作探讨了各种机器学习算法的使用，如支持向量机(SVM)，K最近邻(KNN)，朴素贝叶斯(NB)，决策树(DT)，和多层感知器（MLP）来分析来自DROZY数据库的EEG信号，仔细标记为两种不同的警觉状态（清醒和昏昏欲睡）。分割成10s间隔确保精确检测，而相关的特征选择层增强了准确性和泛化性。所提出的方法实现了99.84％和96.4％的内部（受试者）和内部（跨主题）模式的高准确率，分别。SVM在帧内模式下成为最有效的睡意检测模型，而MLP在中间模式中表现出优异的准确性。这项研究为实施主动嗜睡检测系统以增强各个行业的职业安全提供了有希望的途径。
