Abstract
The prediction of the menstrual cycle phase and fertility window by easily measurable bio-signals is an unmet need and such technological development will greatly contribute to women's QoL. Although many studies have reported differences in autonomic indices of heart rate variability (HRV) between follicular and luteal phases, they have not yet reached the level that can predict the menstrual cycle phases. The recent development of wearable sensors-enabled heart rate monitoring during daily life. The long-term heart rate data obtained by them carry plenty of information, and the information that can be extracted by conventional HRV analysis is only a limited part of it. This chapter introduces comprehensive analyses of long-term heart rate data that may be useful for revealing their associations with the menstrual cycle phase.
TopHeart Rate Variability (Hrv)
It is well known that the autonomic nervous functions in women are affected by the menstrual cycle. Many studies reported the changes in autonomic indices of heart rate variability (HRV) with menstrual cycle (Bai, Li, Zhou, & Li, 2009; Brar, Singh, & Kumar, 2015; de Zambotti, Nicholas, Colrain, Trinder, & Baker, 2013; Guasti et al., 1999; Leicht, Hirning, & Allen, 2003; Sato & Miyake, 2004; Sato, Miyake, Akatsu, & Kumashiro, 1995; Yildirir, Kabakci, Akgul, Tokgozoglu, & Oto, 2002). These studies commonly suggest that, compared to the follicular phase, the luteal phase is accompanied by relative increase in sympathetic activity to cardiac parasympathetic activity. These studies, however, have not yet reached the level that can predict the phases of the menstrual cycle and they have used short-term HRV obtained under well controlled laboratory conditions.
The analysis of HRV is divided into short-term and long-term HRV (Hayano & Yuda, 2019). Typically, the former uses 5-minute data and the later uses 24-h data, but the difference is not only the length of data. Short-term HRV is analyzed from data obtained in a laboratory under strictly controlled conditions, while long-term HRV uses data recorded by wearable sensors in freely moving subjects. The autonomic nervous system is constantly regulating the body systems in response to various external and internal stimuli. Thus, the autonomic indices of HRV also respond sensitively to measurement conditions. For example, the power of high frequency (HF, 0.15-0.4 Hz) component of HRV that reflects cardiac parasympathetic function increases with supine rest and non-REM sleep (Hayano & Yasuma, 2003) and it decreases with standing (Pomeranz et al., 1985), physical activities(Yamamoto, Hughson, & Peterson, 1991), and food intake (Hayano et al., 1990). The HF power is also affected by respiration independently of autonomic neural activity; it increases with slow and deep breathing (Hayano, Mukai, et al., 1994; Hirsch & Bishop, 1981). Also, low frequency (LF, 0.04-0.15 Hz) component, particularly its ratio to HF power (LF/HF), increased with standing (Hayano et al., 2001).
Key Terms in this Chapter
Cyclic Variation of Heart Rate (CVHR): The characteristic pattern of heart rate fluctuation accompanying sleep apnea. CVHR appears as repetitive peaks of heart rate occurring at an interval of 25 to 120 seconds, which correspond to the time of cessation of each episode of sleep apnea occurring periodically.
Heart Rate Variability (HRV): Physiological fluctuations of inter-beat intervals of the heart. HRV is originated from the brain and mediated through the cardiac autonomic nervous system.
Frequency Components of HRV: Spectral analysis of short-term HRV reveals the presence of high-frequency (HF, 0.15-0.4 Hz) and low-frequency (LF, 0.04-0.15 Hz) components. The HF component reflects respiratory fluctuation of heart rate and is mediated purely by the cardiac parasympathetic nerves, while LF component is mediated by both parasympathetic and sympathetic nerves. Long-term HRV also includes ultra-low frequency (ULF, <0.0033 Hz) and very-low frequency (VLF, 0.0033- 0.04 Hz) components in addition to LF and HF components. ULF and VLF components include heart rate fluctuations caused by many factors including body temperature regulation, renin-angiotensin system, and complex fractal fluctuations caused by brain activities.
Nonlinear Components of Heart Rate Fluctuation: Mathematically, nonlinear system is defined as that the change in the output is not proportional to the change of the input. In heart rate fluctuations, nonlinear components are observed as those not forming a peak in power spectrum. In healthy peoples, the power of nonlinear components shows 1/ f distribution, i.e., the power inversely correlated with frequency. This is also known as a characteristic of fractal dynamics, in which values exhibit persistent long-range negative correlations, that is, the larger (smaller) values tend to be followed by smaller (larger) values in all time scales.
Parasympathetic Nervous System: Along with the sympathetic nervous system, one of the two branches of the autonomic nervous system. Although the major role of the cardiac parasympathetic nervous system is to decrease the heart rate, it also has an important role to suppress the occurrence of fatal arrhythmias in the destabilized heart by the sympathetic stimulation.