TESTING FOR NONLINEARITY OF THE CONTRACTION SEGMENTS IN UTERINE ELECTROMYOGRAPHY

Abstract

In this report, we test for possible nonlinearity of the contraction segments interspersed in a uterine electromyography (EMG), recorded externally with abdominal electrodes. There have been several reports in which the uterine contractility had been assumed to be an auto-regressive process and others have hypothesized it as a nonlinear process and possibly chaotic. The surrogate data testing was used successfully to detect nonlinear behavior of physiological systems. However, there have been case studies, which discuss spurious identification of nonrandom structures. The proper choice of the null hypothesis and discriminant statistics plays a crucial role in the surrogate data testing. We have chosen the approximate entropy as the discriminant statistic for our tests. The null hypothesis addressed here is that the uterine contraction is a linearly correlated noise transformed by a nonlinear function. We applied the Amplitude Adjusted Fourier Transform (AAFT) and the Iterated Amplitude Adjusted Fourier Transform (IAAFT) tests to the uterine contraction data. The Kolmogorov Smirnov (D) statistics identified the discriminant values of the surrogates to be from a Gaussian distribution. Parametric testing showed a very low significance value, (~2σ), which indicated the absence of nonrandom structure in the contraction segment.