Vasomotion is the spontaneous time-dependent contraction and relaxation of micro arteries and the oscillating frequency is about 0.01–0.1 Hz. The physiological mechanism of vasomotion has not been thoroughly understood. From the dynamics point of view, the heartbeat is the only external loading exerted on the vascular system. We speculate that the nonlinear vascular system and the variable period of the heartbeat might induce the low-frequency vasomotion. In this study, the laser Doppler flowmeter is used to measure the time series of radial artery blood flow and reconstructed modified time series that has the same period as the measured time series but different heartbeat curves. We measured the time series of radial artery blood flow in different conditions by adding different noise disturbances on the forearm, and we decomposed the experiment pulse signal by Hilbert–Huang transform. The wavelet spectral analyses showed that the low-frequency components were induced by the variable period but independent of the shape of the heartbeat curve. Furthermore, we simulated the linear flow in a single pipe and the nonlinear flow in a piping network and found that the nonlinear flow would generate low-frequency components. From the results, we could deduce that the variable period of heartbeat and the nonlinearity of the vascular system induce vasomotion. The noise has effects on the blood signals related to the respiratory activities (∼0.3 Hz) but little influence on that related to the cardiac activities (∼1 Hz). Adding white noise and then stopping would induce an SNR increase in the frequency band related to vasomotion (∼0.1 Hz).

Vasomotion refers to the spontaneous oscillation of blood vessels within a frequency range of 0.01 to 1.6 Hz. Various disease states, including hypertension and diabetes, have been associated with alterations in vasomotion at the finger, indicating potential impairment of skin microcirculation. Due to the non-linear nature of human vasculature, the modification of vasomotion may vary across different locations for different diseases. In this study, Laser Doppler Flowmetry was used to measure blood flow motion at acupoints LU8, LU5, SP6, and PC3 among 49 participants with or without diabetes and/or hypertension. Fast Fourier Transformation was used to analyze noise type while Hilbert-Huang Transformation and wavelet analysis were applied to assess Signal Noise Ratio (SNR) results. Statistical analysis revealed that different acupoints exhibit distinct spectral characteristics of vasomotion not only among healthy individuals but also among patients with diabetes and hypertension. The results showed strong heterogeneity of vasomotion among blood vessels, indicating that the vasomotion measured at a certain point may not reflect the real status of microcirculation.