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Interpretation of the approximate entropy using fixed tolerance values as a measure of amplitude variations in biomedical signals

机译:使用固定公差值作为生物医学信号中幅度变化的量度来解释近似熵

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A new method for the quantification of amplitude variations in biomedical signals through moving approximate entropy is presented. Unlike the usual method to calculate the approximate entropy (ApEn), in which the tolerance value (r) varies based on the standard deviation of each moving window, in this work ApEn has been computed using a fixed value of r. We called this method, moving approximate entropy with fixed tolerance values: ApEnf. The obtained results indicate that ApEnf allows determining amplitude variations in biomedical data series. These amplitude variations are better determined when intermediate values of tolerance are used. The study performed in diaphragmatic mechanomyographic signals shows that the ApEnf curve is more correlated with the respiratory effort than the standard RMS amplitude parameter. Furthermore, it has been observed that the ApEnf parameter is less affected by the existence of impulsive, sinusoidal, constant and Gaussian noises in comparison with the RMS amplitude parameter.
机译:提出了一种通过移动近似熵量化生物医学信号幅度变化的新方法。与通常的计算近似熵(ApEn)的方法不同(在该方法中,公差值(r)基于每个移动窗口的标准偏差而变化),在这项工作中,ApEn是使用固定值r计算的。我们称此方法为,以固定的容差值移动近似熵:ApEn f 。获得的结果表明ApEn f 允许确定生物医学数据序列中的振幅变化。当使用中间公差值时,可以更好地确定这些幅度变化。对隔膜肌电图信号进行的研究表明,与标准RMS振幅参数相比,ApEn f 曲线与呼吸努力的相关性更高。此外,已经观察到,与RMS振幅参数相比,ApEn f 参数受脉冲,正弦,恒定和高斯噪声的影响较小。

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