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Evaluating MSE Applicability to Short HR Time-Series

机译:评估MSE适用于短时间内系列

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Multiscale entropy is successfully used to measure dynamical complexity of a finite length time series of different physiological data, including the heart rate. It is shown that the multiscale entropy as a measure can be used to discriminate healthy subjects from subjects with pathological conditions. In this paper we evaluate possibility to apply multiscale entropy to shorter heart rate time series and to evaluate resources needed to implement the algorithm in C, and to assess if it is possible to run the algorithm on a specific DSP platform.
机译:多尺度熵成功用于测量有限长度时间序列不同生理数据的动态复杂性,包括心率。结果表明,多尺度熵作为一种措施可用于区分具有病理条件的受试者的健康受试者。在本文中,我们评估了将多尺度熵应用于更短的心率时间序列并评估在C中实现算法所需的资源,并评估在特定DSP平台上的算法。

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