Effect of decimation on the classification rate of non-linear analysis methods applied to uterine EMG signals

Abstract

Recently, much attention has been paid to the use of non-linear analysis techniques for the characterization of biological signals. Several measures have been proposed to detect non-linear characteristics in time series. The effect of sampling frequency on the performance of these non-linear methods has rarely been evaluated. In this paper, we present a preliminary study of this effect for four methods that are widely used in non-linearity detection: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. These methods have been applied to real uterine EMG signals with the aim of distinguishing between pregnancy and labor contractions. The signals were used to classify contractions before and after decimating them by a factor 10. The results show that decimation improves the performance for sample entropy. It reduces it considerably for Delay Vector Variance and only slightly for Time reversibility and Lyapunov exponents. Time reversibility still gives the highest classification rate. The methods were much less computationally expensive after down sampling.