In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.

中文翻译：

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基于心率变异性信号分析的滑动时间窗大小选择对麻醉下有害刺激智能监测的影响

近几十年来，在麻醉下对疼痛和有害刺激的客观评价进展甚微。一些基于医学信号的研究未能提供对这个问题的一般理解。本文提出了一种心率变异性信号的特征提取方法，旨在进一步提高对有害刺激的评价。在数据处理过程中，采用经验模态分解对心率变异性信号进行分解和重组，采用滑动时间窗法分别提取有害刺激的信号特征。通过改变窗口大小来研究窗口大小对特征提取的影响。通过比较结果，数据处理过程中的特征提取是有价值的，并且窗口大小的选择有很大的影响。随着所选窗口大小的增加，我们可以获得更好的检测结果。但是对于窗口大小的最佳选择，为了保证结果的准确性并使其易于使用，那么，我们需要得到一个合适的窗口大小。