Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina.
Med Eng Phys. 2014 Feb;36(2):244-9. doi: 10.1016/j.medengphy.2013.07.011. Epub 2013 Aug 20.
Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.
困意是许多交通事故的主要原因之一，因为驾驶员的注意力和对危险的识别明显下降，车辆操控能力降低。本研究旨在开发一种使用时间、频谱和小波分析自动检测脑电图记录中困倦阶段的方法。总共从一个脑电图通道计算了 19 个特征，以区分警觉和困倦阶段。在基于威尔克斯准则的 lambda 的选择过程之后，选择了 7 个参数来为神经网络分类器提供信息。分析了 18 个脑电图记录。该方法对警觉和困倦阶段的正确检测率分别为 87.4%和 83.6%。结果表明，这些参数可以区分两个阶段。特征易于计算，可以实时获得。这些变量可以用于车辆中的自动困倦检测系统，从而降低因驾驶员困倦而导致事故的发生率。
