Zhang Xuan, Wang Dixin, Wu Hongtong, Chao Jinlong, Zhong Jitao, Peng Hong, Hu Bin
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Comput Methods Programs Biomed. 2023 Dec;242:107773. doi: 10.1016/j.cmpb.2023.107773. Epub 2023 Sep 3.
With a large number of accidents caused by the decline in the vigilance of operators, finding effective automatic vigilance monitoring methods is a work of great significance in recent years. Based on physiological signals and machine learning algorithms, researchers have opened up a path for objective vigilance estimation.
Sparse representation (SR)-based recognition algorithms with excellent performance and simple models are very promising approaches in this field. This paper aims to study the adaptability and performance improvement of truncated l distance (TL1) kernel on SR-based algorithm in the context of physiological signal vigilance estimation. Compared with the traditional radial basis function (RBF), the TL1 kernel has good adaptiveness to nonlinearity and is suitable for the discrimination of complex physiological signals. A recognition framework based on TL1 and SR theory is proposed. Firstly, the inseparable physiological features are mapped to the reproducing kernel Kreĭn space through the infinite-dimensional projection of the TL1 kernel. Then the obtained kernel matrix is converted into the symmetric positive definite matrix according to the eigenspectrum approaches. Finally, the final prediction result is obtained through the sparse representation regression process.
We verified the performance of the proposed framework on the popular SEED-VIG dataset containing physiological signals (electroencephalogram and electrooculogram) associated with vigilance. In the experimental results, the TL1 kernel is superior to the RBF kernel in both performance and kernel parameter stability.
This demonstrates the effectiveness of the TL1 kernel in distinguishing physiological signals and the excellent vigilance estimation capability of the proposed framework. Moreover, the contribution of our research motivates the development of physiological signal recognition based on kernel methods.
由于大量事故是由操作员警惕性下降导致的，近年来寻找有效的自动警惕性监测方法具有重大意义。基于生理信号和机器学习算法，研究人员开辟了一条客观警惕性估计的道路。
基于稀疏表示（SR）的识别算法性能优异且模型简单，是该领域非常有前景的方法。本文旨在研究截断l距离（TL1）核在基于SR的算法在生理信号警惕性估计背景下的适应性和性能提升。与传统径向基函数（RBF）相比，TL1核对非线性具有良好的适应性，适用于复杂生理信号的判别。提出了一种基于TL1和SR理论的识别框架。首先，通过TL1核的无穷维投影将不可分离的生理特征映射到再生核Kreĭn空间。然后根据特征谱方法将得到的核矩阵转换为对称正定矩阵。最后，通过稀疏表示回归过程得到最终预测结果。
我们在包含与警惕性相关的生理信号（脑电图和眼电图）的流行SEED-VIG数据集上验证了所提出框架的性能。在实验结果中，TL1核在性能和核参数稳定性方面均优于RBF核。
这证明了TL1核在区分生理信号方面的有效性以及所提出框架出色的警惕性估计能力。此外，我们的研究贡献推动了基于核方法的生理信号识别的发展。
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