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中文摘要：
使用Costa等提出的算法研究心电图（ECG）的多尺度样本熵（MSE）的特性。研究发现，心电图MSE均值随着心脏健康状况的变差呈下降趋势。健康人的心电图的MSE波动范围比较稳定在某个范围之内；而冠心病人的波动范围则相对较大；心梗病人的MSE变动范围比较小，波动范围收敛在相对低的多尺度熵值区域。研究表明MSE均值和波动范围的变动情况可以比较有效地揭示心脏健康状况，尤其是MSE波动范围的大小变化情况表征了心脏疾病演化的动态过程（在疾病形成之中其熵值的波动范围变化最大），是一个早期发现病症较为灵敏的参数，具有重要的临床诊断意义。
英文摘要：
Costa and others proposed algorithm were used to study muhiscale sample entropy （MSE） features of ECG. It was found that, from a medical statistics, the average MSE of the ECG was in a declining trend with the state of deterioration of heart health. For the fluctuations in MSE of ECG, the healthy subjects＇ were in a relatively stable scope, and the subjects＇ with coronary heart disease were of relatively larger changes; and the subjects＇ with myocardial infarction were of relatively smaller changes, fluctuations were in the convergence of the relatively low MSE region. It was showed that the changes of the average MSE and that of the MSE fluctuation scopes could be more effective in revealing the heart health status. Especially, the amplitude of the fluctuations in MSE characterized the dynamic process of the heart disease evolution, which is a more sensitive parameter for early diagnosis.
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