Key result
The multivariate multiscale fuzzy entropy (MMFE) algorithm improved the classification accuracy of term-preterm deliveries compared to MMSE, achieving a maximum AUC of 0.99.
Why the study?
Does the multivariate multiscale fuzzy entropy (MMFE) algorithm improve classification accuracy of term-preterm deliveries compared to MMSE in uterine EMG signals?
Does the multivariate multiscale fuzzy entropy (MMFE) algorithm improve classification accuracy of term-preterm deliveries compared to MMSE in uterine EMG signals?
Effect estimate: AUC 0.99
The proposed MMFE algorithm demonstrates superior performance over MMSE in analyzing short-duration uterine EMG signals, achieving high accuracy in classifying term versus preterm deliveries.
May improve preterm monitoring via MMFE on EMG; leaves open need for clinical validation trials.
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.
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Ahmed et al. (2016) studied Term-preterm deliveries. Multivariate multiscale fuzzy entropy (MMFE) algorithm vs. Multivariate multiscale entropy (MMSE) was evaluated on Classification accuracy of term-preterm deliveries (AUC 0.99). The multivariate multiscale fuzzy entropy (MMFE) algorithm improved the classification accuracy of term-preterm deliveries compared to MMSE, achieving a maximum AUC of 0.99.
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