Testing for nonlinearity of the contraction segments in uterine electromyography

In this report, we test for possible nonlinearity of the contraction segmen ts interspersed in a uterine electromyography (EMG), recorded externally wi th abdominal electrodes. There have been several reports in which the uteri ne contractility had been assumed to be an auto-regressive process and othe rs have hypothesized it as a nonlinear process and possibly chaotic. The su rrogate 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 sur rogate data testing. We have chosen the approximate entropy as the discrimi nant statistic for our tests. The null hypothesis addressed here is that th e uterine contraction is a linearly correlated noise transformed by a nonli near function. We applied the Amplitude Adjusted Fourier Transform (AAFT) a nd the Iterated Amplitude Adjusted Fourier Transform (IAAFT) tests to the u terine contraction data. The Kolmogorov Smirnov (D) statistics identified t he discriminant values of the surrogates to be from a Gaussian distribution . Parametric testing showed a very low significance value, (similar to 2 si gma), which indicated the absence of nonrandom structure in the contraction segment.