■这项研究探索了一种通过分析脑电图(EEG)信号来检测唤醒水平的新颖方法。利用来自18名健康参与者的数据的Faller数据库,我们采用64通道脑电图系统。
■我们采用的方法需要从每个通道中提取十个频率特性,为每个信号实例计算640维的特征向量。为了提高分类准确性,我们采用遗传算法进行特征选择,将其视为多目标优化任务。该方法利用快速比特跳变来提高效率,克服传统的位串限制。混合算子加快算法收敛,和解决方案选择策略识别最合适的特征子集。
■实验结果证明了该方法在检测不同状态的唤醒水平方面的有效性,随着准确性的提高,灵敏度,和特异性。在方案一,所提出的方法达到了平均精度,灵敏度,和93.11%的特异性,98.37%,99.14%,分别。在场景2中,平均值为81.35%,88.65%,和84.64%。
■获得的结果表明,所提出的方法在不同场景中具有很高的检测唤醒水平的能力。此外,已经证明了采用所提出的特征减少方法的优点。
UNASSIGNED: This study explores a novel approach to detecting arousal levels through the analysis of electroencephalography (EEG) signals. Leveraging the Faller database with data from 18 healthy participants, we employ a 64-channel EEG system.
UNASSIGNED: The approach we employ entails the extraction of ten frequency characteristics from every channel, culminating in a feature vector of 640 dimensions for each signal instance. To enhance classification accuracy, we employ a genetic algorithm for feature selection, treating it as a multiobjective optimization task. The approach utilizes fast bit hopping for efficiency, overcoming traditional bit-string limitations. A hybrid operator expedites algorithm convergence, and a solution selection strategy identifies the most suitable feature subset.
UNASSIGNED: Experimental results demonstrate the method\'s effectiveness in detecting arousal levels across diverse states, with improvements in accuracy, sensitivity, and specificity. In scenario one, the proposed method achieves an average accuracy, sensitivity, and specificity of 93.11%, 98.37%, and 99.14%, respectively. In scenario two, the averages stand at 81.35%, 88.65%, and 84.64%.
UNASSIGNED: The obtained results indicate that the proposed method has a high capability of detecting arousal levels in different scenarios. In addition, the advantage of employing the proposed feature reduction method has been demonstrated.