Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring.
Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring.
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DOI：
10.3390/e22030319
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发表时间：
2020-03-11
期刊：
影响因子：
--
通讯作者：
Escudero J
中科院分区：
文献类型：
--
作者：
Kafantaris E;Piper I;Lo TM;Escudero J
熵量化算法通过有效测量生物信号中的不规则性而成为用于个体的生理监测的突出工具。然而，为了确保它们在监测应用中的有效适应，这些算法的性能在分析包含缺失和异常样本的时间序列时需要是鲁棒的，这在生理监测设置（例如可穿戴设备和重症监护病房）中是常见的。本文的重点是增加分散熵（DisEn），通过引入新的变化，在这样的应用中，提高性能的算法。在不同的实验设置下测试原始算法及其变化，这些实验设置在心率间隔、脑电图和呼吸阻抗时间序列上复制。我们的研究结果表明，DisEn的算法变化实现了相当大的性能改善，而我们的分析表明，在与以前的研究一致，离群样本可以有一个重大影响的熵量化算法的性能。因此，所提出的变化可以通过减轻缺失和离群样本的破坏性影响来帮助将DisEn实施到生理监测应用中。
Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.
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