Verbal and spatial skills, like word memorisation and navigation, remain consistent throughout a person's menstrual cycle, suggesting menstruation has little effect on these cognitive functions. Previous research has suggested that the menstrual cycle may affect cognition due to hormonal fluctuations. For instance, studies have shown brain regions critical for spatial processing and memory change in size as the concentration of certain hormones ebbs and flows.

Verbal and spatial skills, like word memorisation and navigation, remain consistent throughout a person's menstrual cycle, suggesting menstruation has little effect on these cognitive functions. Previous research has suggested that the menstrual cycle may affect cognition due to hormonal fluctuations. For instance, studies have shown brain regions critical for spatial processing and memory change in size as the concentration of certain hormones ebbs and flows.

A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman's life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.

A smart ring designed exclusively for women will do what no husband would ever dream of – tell them how to snap out of their mood. The Evie ring will monitor the wearer's menstrual cycles, sleep patterns, and other vital statistics in a bid to help her'learn how to feel her best'. Rather than provide the data in complex graphs and charts, the results will instead be simplified into'actionable insights' for the user to change their lifestyle. The Californian-based firm behind the smart ring, Movano, is aiming for it to become the first wearable to also be approved as a medical device. The Evie ring will monitor the wearer's menstrual cycles, sleep patterns, and other vital statistics in a bid to help her'learn how to feel her best' Alongside monitoring heart rate, respiration rate, and skin temperature, the ring will also track users' ovulation, periods, and menstrual symptoms.

It's often said that men and women's brains work so differently that one sex is from Venus and the other is from Mars. Well now a new study supports this hypothesis after finding 1,000 genes that are much more active in one gender than the other. It looked into how male and female mouse brains differ by probing areas that are known to program'rating, dating, mating and hating' behaviours. The behaviours -- for example, male mice's quick determination of a stranger's sex, females' receptivity to mating, and maternal protectiveness -- help the animals reproduce and their offspring survive. These differences are also likely reflected in the brains of men and women, the researchers from Stanford Medicine said.

Eveline was developed based on the personal experience of Eveline co-founder and CTO Carson Chen and his wife. While movies such as the Terminator films raise the specter that artificial intelligence (AI) might one day eliminate humans, in the real world machine learning algorithms are now being employed to help couples experiencing infertility issues make sure there are more humans than ever. Eveline has brought to market a namesake ovulation prediction kit in the form of a smartphone application that leverages machine learning algorithms to more precisely determine when a woman is most likely to conceive. The kit itself consists of an adapter that attaches to a smartphone camera that is then employed to analyze urine samples. The algorithms within the application will then example samples through two menstrual cycles before starting to make suggestions concerning when the optimal time to conceive is for any couple.

We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns. Reconstructing and forecasting the evolution of hormonal dynamics is a challenging task, but a critical one to improve general understanding of the menstrual cycle and personalized detection of potential health issues. Our goal is to infer and forecast individual hormone daily levels over time, while accommodating pragmatic and minimally invasive measurement settings. To that end, our approach combines the power of probabilistic generative models (i.e., multi-task Gaussian processes) with the flexibility of neural networks (i.e., a dilated convolutional architecture) to learn complex temporal mappings. To attain accurate hormone level reconstruction with as little data as possible, we propose a sampling mechanism for optimal reconstruction accuracy with limited sampling budget. Our results show the validity of our proposed hormonal dynamic modeling framework, as it provides accurate predictive performance across different realistic sampling budgets and outperforms baselines methods.

The startup that claims responsibility for the conception of 10,000 babies has raised $30million in Series B fundraising to expand into other aspects of female health. The Ava bracelet is a $249 wearable device with an accompanying app that helps women track their fertility cycles. Designed to be worn overnight, the technology measures nine physiological variables to monitor a woman's fertility or pregnancy. The funding comes from previous unnamed investors as well as new European venture capital participants Btov and SVC. In late 2016, Ava raised a Series A round of $9.7million in funding.

In this paper, we introduce a novel task for machine learning in healthcare, namely personalized modeling of the female hormonal cycle. The motivation for this work is to model the hormonal cycle and predict its phases in time, both for healthy individuals and for those with disorders of the reproductive system. Because there are individual differences in the menstrual cycle, we are particularly interested in personalized models that can account for individual idiosyncracies, towards identifying phenotypes of menstrual cycles. As a first step, we consider the hormonal cycle as a set of observations through time. We use a previously validated mechanistic model to generate realistic hormonal patterns, and experiment with Gaussian process regression to estimate their values over time. Specifically, we are interested in the feasibility of predicting menstrual cycle phases under varying learning conditions: number of cycles used for training, hormonal measurement noise and sampling rates, and informed vs. agnostic sampling of hormonal measurements. Our results indicate that Gaussian processes can help model the female menstrual cycle. We discuss the implications of our experiments in the context of modeling the female menstrual cycle.

Women have long claimed their monthly period makes them more irritable or stressed. Now, however, scientists have said the idea that a woman's menstrual cycle affects her thinking is nothing more than a myth. According to some previous studies, women are more impulsive and moody before their period, and more'rational' afterwards. Scientists have said the idea that a woman's menstrual cycle affects her thinking is nothing more than a myth But the latest research says that while women may feel their thinking became altered, this was not the case when studied scientifically. It seems that despite hormone levels fluctuating enormously in a woman's body, they have no effect on her ability to remember or make decisions.