A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis
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DOI：
10.3390/e19010002
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发表时间：
2017-01-01
期刊：
影响因子：
2.7
通讯作者：
Mandic, Danilo P.
中科院分区：
文献类型：
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作者：
Ahmed, Mosabber U.;Chanwimalueang, Theerasak;Mandic, Danilo P.
最近推出的多变量多尺度熵（MMSE）已成功地用于量化结构复杂性的非线性通道内和跨通道的相关性，以及揭示复杂的动力学耦合和不同程度的同步在多个尺度在现实世界中的多通道数据。然而，MMSE的适用性是有限的粗粒化过程中定义的尺度，因为它连续减少每个尺度的数据长度，从而产生不准确和不确定的熵估计在较高的尺度和短长度的数据。为此，我们提出了多变量多尺度模糊熵（MMFE）算法，并证明其优越性的MMSE合成以及真实世界的子宫肌电图（EMG）的短时间信号。基于MMFE特征，实现了足月-早产分类准确性的提高，最大曲线下面积（AUC）值为0.99。
The recently introduced multivariate multiscale entropy (MMSE) has been successfully used to quantify structural complexity in terms of nonlinear within-and cross-channel correlations as well as to reveal complex dynamical couplings and various degrees of synchronization over multiple scales in real-world multichannel data. However, the applicability of MMSE is limited by the coarse-graining process which defines scales, as it successively reduces the data length for each scale and thus yields inaccurate and undefined entropy estimates at higher scales and for short length data. To that cause, we propose the multivariate multiscale fuzzy entropy (MMFE) algorithm and demonstrate its superiority over the MMSE on both synthetic as well as real-world uterine electromyography (EMG) short duration signals. Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99.