Can SVM be used for automatic EEG detection of drowsiness during car driving?
Can SVM be used for automatic EEG detection of drowsiness during car driving?
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DOI：
10.1016/j.ssci.2008.01.007
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发表时间：
2009-01-01
期刊：
影响因子：
6.1
通讯作者：
Wilder-Smith, Einar P. V.
中科院分区：
文献类型：
--
作者：
Yeo, Mervyn V. M.;Li, Xiaoping;Wilder-Smith, Einar P. V.
本研究旨在开发自动检测驾驶时困倦的方法。支持向量机（SVM）代表了它基于模式识别的优越信号分类工具。 SVM 在识别和区分警觉状态和昏昏欲睡状态之间发生的脑电图 (EEG) 变化方面的有用性已经过测试。二十名人类受试者接受了脑电图监测的驾驶模拟。警觉脑电图以β活动占主导地位为标志，而昏昏欲睡的脑电图则以α活动缺失为标志。眨眼的持续时间与快速和慢速眨眼相关的警觉水平很好地对应。来自两个州的 EEG 数据样本被用来训练 SVM 程序，方法是使用 4 个主要频段的 4 个频率特征的区分标准。经过训练的 SVM 程序使用未分类的脑电图数据进行了测试，并随后检查与手动分类的一致性。分类准确率达到99.3%。 SVM 程序还能够在超过 90% 的数据样本中可靠地预测从警觉到困倦的转变。这项研究表明，SVM 可以自动分析和检测 EEG 变化，并且 SVM 是开发用于驾驶安全的先发性自动睡意检测系统的良好候选者。 (C) 2008 Elsevier Ltd. 保留所有权利。
This study aims to develop,in automatic method to detect drowsiness onset while driving. Support vector machines (SVM) represents it superior signal classification tool based on pattern recognition. The usefulness of SVM in identifying and differentiating electroencephalographic (EEG) changes that occur between alert and drowsy states wits tested. Twenty human subjects underwent driving simulations with EEG monitoring. Alert EEG was marked by dominant beta activity, while drowsy EEG was marked by alpha dropouts. The duration of eye blinks corresponded well with alertness levels associated with fast and slow eye blinks. Samples of EEG data from both states were used to train the SVM program by using it distinguishing criterion of 4 frequency features across 4 principal frequency bands. The trained SVM program was tested oil unclassified EEG data and subsequently checked for concordance with manual classification. The classification accuracy reached 99.3%. The SVM program was also able to predict the transition from alertness to drowsiness reliably in over 90% of data samples. This study shows that automatic analysis and detection of EEG changes is possible by SVM and SVM is it good candidate for developing pre-emptive automatic drowsiness detection systems for driving safety. (C) 2008 Elsevier Ltd. All rights reserved.