Search

This article is a comprehensive review of recent studies of the authors on the indication of mental health from biological information contained in pulse waves. A series of studies discovered that the largest Lyapunov exponent (LLE) of the attractor, which is constructed for the time series data from pulse waves, can provide as an effective indicator of mental health. A low level of LLE indicates insufficiency of external adaptability, which is characteristic of dementia and depression sufferers. On the contrary, a continuous high level of LLE indicates excessive external adaptability, and people in this condition tend to resort to violence. With this discovery, real-time display of the LLE, combined with other physiological indexes such as the autonomic nerve balance (ANB), sample entropy, and vascular age, as a reference, can enable people to conduct self-check of mental status. To this end, software development was performed in order to enable users to conduct pulse wave measurement anywhere at any time and display the analytical results in real time during the measurement.

While there are plenty of studies on clinical diagnosis of Parkinson’s disease, little literature is available on the possibility of simple tests that can help distinguish Parkinson’s disease sufferers from healthy individuals. In our study, by making use of pulse wave data, we identify physiological patterns characteristic of Parkinson’s disease patients. We observe that the sample entropy values of pulse waves, with certain parameters fixed, is statistically different between Parkinson’s disease sufferers and healthy individuals. We also find significant difference between the two groups in values of the largest Lyapunov exponent computed from the same pulse wave data. In addition, we introduce an Android tablet that in which the real-time measurement and analysis functions are incorporated. With this device, it takes only 5 s to produce a test result.

In this study, DUAL measurements of finger pulse waves were taken from three children (Child A, Child B, and Child C) and their nursery teacher longitudinally (when children were 3 years old and 4 years old). Behaviors of difficult children were evaluated and scored by using "Checklist for Difficult Children" (S. Shimano, 2014) [1]. Four subscales of "troubles (anti-social behaviors)," "dissocial behaviors," "autism behaviors," and "hyperactive behaviors" were evaluated with "Checklist for Difficult Children." Three children who exhibited high scores for all four subscales were selected for the study. According to the nursery teacher, Child A was diagnosed with autism spectrum disorder. Child B's mother had an attitude problem in childrearing, so the child was not disciplined by her. And Child C showed signs of attachment disorder. DUAL measurements were taken from Child A, Child B, and Child C, respectively paired with their nursery teacher, to visualize any mental interactions with the teacher.

In this research, we obtain various indicators by analyzing pulse waves, which are then used to self-cognize one's own psychological state and characteristics. Pulse wave measurement is simple and safe. What's more, we can get three indicators by analyzing the measured data: the largest Lyapunov exponent (LLE), autonomic nerve balance (ANB), and sample entropy (SampEn). Using these three indicators, we conducted research on a number of factors, including the discrimination of depression, the psychological state analysis of Alzheimer's disease, the effects of Parkinson's disease on indicators, and the change of mental state after long-term study. However, in order to utilize this technology more scientifically, we need to assemble a more complete database and make more systematic measurements.

This project seeks to establish a measurement system that uses physiological indicators to reflect learning efficiency and changes in mental state during instruction. For this study, we have chosen two indicators: the maximal largest Lyapunov exponent (LLE) and the autonomic nervous balance (ANB). The former indicates degrees of stress and change in the individual, while the latter controls organ movement and reflects one's stress state. Both are measured via pulse waves and are obtained through nonlinear calculation. Three students were observed during a 4-6 hour period of learning, during which time pulse waves were measured every 1-2 hours to enable changes in the two indicators to be recorded. The results show that the LLE declines as the study period progresses, while ANB levels fluctuate.

Thus far, attention has been paid to phenomena wherein respiratory movement and physical movement systems have worked in coordination with one another, which has been studied. On the other hand, the earlier studies have hinted at the importance of considering mental and physical health from a holistic perspective, while taking into consideration the principles that prescribe the chaotic behavior of living organisms. In other words, this raises the issue: The validity of hypothesizing chaos dynamics as a universal principle for living systems that prescribes the chaotic nature of center of gravity sway and respiration. Therefore, the purpose of this study is to focus on center of gravity sway and respiration in order to examine the relationship in the chaotic nature between the two. Consequently, the issue presented above was supported. What this implies is that quantitatively assessing synchronous phenomena for the LLE for center of gravity sway and respiration used in this study is a more highly valid approach for determining people's mental and physical conditions in the field of clinical medicine, and therefore it could potentially be applied as a means of substantially supporting the promotion of health.