Zayed Aymen, Belhadj Nidhameddine, Ben Khalifa Khaled, Valderrama Carlos, Bedoui Mohamed Hedi
Service d'électronique et de Microélectronique, University of Mons, 7000 Mons, Belgium.
Research Laboratory of Technology and Medical Imaging-LTIM-LR12ES06, Faculty of Medicine of Monastir, Monastir 5000, Tunisia.
Sensors (Basel). 2025 Sep 5;25(17):5530. doi: 10.3390/s25175530.
Drowsiness constitutes a significant risk factor in diverse occupational settings, including healthcare, industry, construction, and transportation, contributing to accidents, injuries, and fatalities. Electroencephalography (EEG) signals, offering direct measurements of brain activity, have emerged as a promising modality for drowsiness detection. However, the inherent non-stationary nature of EEG signals, coupled with substantial inter-subject variability, presents considerable challenges for reliable drowsiness detection. To address these challenges, this paper proposes a hybrid approach combining convolutional neural networks (CNNs), which excel at feature extraction, and support vector machines (SVMs) for drowsiness detection. The framework consists of two modules: a CNN for feature extraction from EEG scalograms generated by the Continuous Wavelet Transform (CWT), and an SVM for classification. The proposed approach is compared with 1D CNNs (using raw EEG signals) and transfer learning models such as VGG16 and ResNet50 to identify the most effective method for minimizing inter-subject variability and improving detection accuracy. Experimental evaluations, conducted on the publicly available DROZY EEG dataset, show that the CNN-SVM model, utilizing 2D scalograms, achieves an accuracy of 98.33%, outperforming both 1D CNNs and transfer learning models. These findings highlight the effectiveness of the hybrid CNN-SVM approach for robust and accurate drowsiness detection using EEG, offering significant potential for enhancing safety in high-risk work environments.
嗜睡在包括医疗保健、工业、建筑和运输等多种职业环境中都是一个重要的风险因素，会导致事故、伤害和死亡。脑电图（EEG）信号能够直接测量大脑活动，已成为一种很有前景的嗜睡检测方式。然而，EEG信号固有的非平稳特性，再加上个体间的巨大差异，给可靠的嗜睡检测带来了相当大的挑战。为应对这些挑战，本文提出一种将擅长特征提取的卷积神经网络（CNN）与用于嗜睡检测的支持向量机（SVM）相结合的混合方法。该框架由两个模块组成：一个用于从连续小波变换（CWT）生成的EEG小波图中提取特征的CNN，以及一个用于分类的SVM。将所提出的方法与一维CNN（使用原始EEG信号）以及诸如VGG16和ResNet50等迁移学习模型进行比较，以确定最小化个体间差异并提高检测准确率的最有效方法。在公开可用的DROZY EEG数据集上进行的实验评估表明，利用二维小波图的CNN - SVM模型实现了98.33%的准确率，优于一维CNN和迁移学习模型。这些发现凸显了混合CNN - SVM方法在使用EEG进行稳健且准确的嗜睡检测方面的有效性，为提高高风险工作环境中的安全性提供了巨大潜力。
