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Effect of Filtering on the Classification Rate of Nonlinear Analysis Methods Applied to Uterine EMG Signals

Abstract : Nonlinear time series analysis can provide useful information regarding nonlinear features of biological signals. The effect of filtering on the performance of nonlinear methods is not well-understood. In this work, we investigate the effects of signal filtering on the sensitivity of four nonlinear methods: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. These methods were applied to uterine EMG signals with the aim of using them to discriminate between pregnancy and labor contractions. The signals were filtered using three different band-pass filters before the application of the methods. Results showed that the sensitivity of some methods such as sample entropy was significantly improved with filtering. On the other hand, filtering had little effect on some other methods such as time reversibility. This study concludes that while filtering increases computation time, it may be necessary for some nonlinear methods particularly those with low sensitivity
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01304744
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, April 20, 2016 - 11:53:37 AM
Last modification on : Wednesday, July 15, 2020 - 6:12:01 PM

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  • HAL Id : hal-01304744, version 1

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Ahmad Diab, Omar Falou, Mahmoud Hassan, Brynjar Karlsson, Catherine Marque. Effect of Filtering on the Classification Rate of Nonlinear Analysis Methods Applied to Uterine EMG Signals. 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2015, Milan, Italy. pp.4182--4185. ⟨hal-01304744⟩

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