探测数据缺失情况下心率变异性信号的复杂性
摘要
Data are frequently missing in the analysis of large databases, such as those for census, environmental, and medical longitudinal studies, causing significant difficulties for data analysis and application. And it is very important to maintain stability of analysis results in the case of missing data when determining the quality of a data processing method. To simulate the case of general data loss in this paper, we developed an approach to generate synthetic signals, which randomly wiped data from the original dataset according to a Gaussian distribution and an exponential distribution. We then used two methods of entropy measure-base-scale entropy and approximate entropy-to perform a complexity comparative analysis. Research has shown that two key parameters-the percentage and average length of missing data segments-can cause changes to sequence complexity. However, no matter what kind of distribution is used for the length of the missing data segments, it has little effect on the results of the analysis. Moreover, approximate entropy has strong sensitivity to missing data, so it was unsuitable for the analysis of signals with missing data. However, the calculation results had preferable stability for base-scale entropy, whose superiority suits it for analysis of actual signals.