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Optimal parameters study for sample entropy-based atrial fibrillation organization analysis

机译:基于样本熵的房颤组织分析的最佳参数研究

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摘要

Sample entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended and widely used in the literature for the application of SampEn to some physiological time series, such as heart rate, hormonal data, etc. However, no guidelines exist for the selection of that values in other cases. Therefore, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In the present work, a thorough analysis on the optimal values for m, r and N is presented within the context of atrial fibrillation (AF) organization estimation, computed from surface electrocardiogram recordings. Recently, the evaluation of AF organization through SampEn, has revealed clinically useful information that could be used for a better treatment of this arrhythmia. The present study analyzed optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF. As a result, interesting recommendations about the selection of m, r and N, together with the relationship between N and the sampling rate (fs) were obtained. More precisely, (i) the proportion between N and/s should be higher than 1 s and/s > 256 Hz, (ii) overlapping between adjacent N-length windows does not improve AF organization estimation with respect to the analysis of non-overlapping windows, and (iii) values of m and r maximizing successful classification for the analyzed AF databases should be considered within a range wider than the proposed in the literature for heart rate analysis, i.e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data.
机译:样本熵(SampEn)是一个非线性规律性指标,需要先验选择三个参数:要比较的序列的长度m,模式相似性公差r和要分析的样本数N。对于m,r和N,已经推荐并将其广泛用于将SampEn应用于某些生理时间序列(如心率,激素数据等)的文献中。但是,在其他情况下,没有选择该值的指南。因此,对于将SampEn应用于以前未分析过的生物医学信号,应该需要进行最佳参数研究。在目前的工作中,在根据表面心电图记录计算的心房颤动(AF)组织估计的背景下,对m,r和N的最佳值进行了全面分析。最近,通过SampEn对AF组织的评估揭示了临床有用的信息,这些信息可用于更好地治疗这种心律不齐。本研究分析了房颤组织估计的两种不同情况下的最佳SampEn参数值,例如阵发性房颤终止的预测和持续性房颤的电复律结果。结果,获得了有关选择m,r和N以及N和采样率(fs)之间关系的有趣建议。更准确地说,(i)N和/ s之间的比例应高于1 s和/ s> 256 Hz,(ii)相邻N长度窗口之间的重叠相对于非重叠的窗口,以及(iii)最大化分析的AF数据库成功分类的m和r的值应在比心率分析文献中建议的范围宽的范围内,即m = 1和m = 2,且r在0.1之间数据的标准偏差的0.25倍。

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