心率检测:分数方法和经验模式分解

Ljubica Cimeša, Nenad Popovic, N. Miljković, T. Šekara
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引用次数: 1

摘要

比较了两种用于分离心电图和肌电信号的方法:经验模式分解(EMD)和分数阶微积分(FC)。为了进行定量评价,计算波峰因子(CF)和信噪比(SNR)值,CF和SNR值越高越好。结果表明,CF可以部分评估FC和EMD的性能,表明使用EMD方法可以更好地消除9.72%的伪影。对于FC和EMD,分别提出和讨论了分数阶的自动应用和内模函数的选择。综上所述,建议加强建议的过滤评价,以获得更合适的FC和EMD方法评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heart rate detection: Fractional approach and empirical mode decomposition
The comparison of two methods used to separate electrocardiography and electromyography signals: Empirical Mode Decomposition (EMD) and Fractional Order Calculus (FC) is presented. For quantitative evaluation, crest factor (CF) and signal-to-noise ratio (SNR) values were calculated, where higher CF and SNR values were preferable. Results suggest that FC and EMD performance can be partially assessed by CF indicating better artifact cancellation with EMD approach by 9.72%. For FC and EMD, automatic fractional order application and intrinsic mode function selection were suggested and discussed, respectively. In summary, the results suggested that proposed filtering assessment should be enhanced for more appropriate FC and EMD method's assessment.
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