Possibility of Mental Health Self-Checks using Divergence Properties of Pulse Waves

We conducted a nonlinear analysis of fingertip pulse waves and found that the Lyapunov exponent having the “divergence” property of attractor trajectory was an effective index for estimating human mental health. We showed that this method is effective for an early detection of dementia and depression, as well as in the detection of changes in mental status. In addition, based on these results obtained from time series analysis of the recorded pulse waves, we developed an application device allowing easily installed and convenient measurement for daily check and monitoring mental/physical status. It was an easy-to-use and cost-less device installed in a PC mouse. Also, we studied a representation method of constellation graphs to disclose the fluctuation details of the Lyapunov exponents. In the representation, changes in mental status were assessed and graphically visible by using of the fluctuation factor of the Lyapunov exponents.

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