  The menstrual cycle is composed of the follicular phase and subsequent luteal
phase based on events occurring in the ovary. Basal body temperature (BBT)
reflects this biphasic aspect of menstrual cycle and tends to be relatively low
during the follicular phase. In the present study, we proposed a state-space
model that explicitly incorporates the biphasic nature of the menstrual cycle,
in which the probability density distributions for the advancement of the
menstrual phase and that for BBT switch depend on a latent state variable. Our
model derives the predictive distribution of the day of the next menstruation
onset that is adaptively adjusted by accommodating new observations of BBT
sequentially. It also enables us to obtain conditional probabilities of the
woman being in the early or late stages of the cycle, which can be used to
identify the duration of follicular and luteal phases, as well as to estimate
the day of ovulation. By applying the model to real BBT and menstruation data,
we show that the proposed model can properly capture the biphasic
characteristics of menstrual cycles, providing a good prediction of the
menstruation onset in a wide range of age groups. An application to a large
data set containing 25,622 cycles provided by 3,533 woman subjects further
highlighted the between-age differences in the population characteristics of
menstrual cycles, suggesting wide applicability of the proposed model.
