Time Detection for Ovulation in a Cycle in Presence of Polycystic Ovary Syndrome

Abstract

We study the body temperature variation in four menstrual phases of an individual in presence of Polycystic Ovary Syndrome (PCOS). From the temperature data recorded, we identify the time of ovulation when the cycles are not regular. We obtain growth curve of body temperature by lowess regression. Proliferation rate $\frac{d}{dt}\log y(t)$ of body temperature $y=y(t)$ at time t,  attains the lowest value near the time of ovulation. Temperature residuals from the growth curves are seen to follow a correlated Gaussian process. Some convergence results of empirical distribution functions used in this context are also discussed. Detection of ovulation time may help the individual to plan in conceiving a child.