Artery stiffness is a main factor causing the various cardiovascular diseases in physiology and pathology. Therefore, the development of the non-invasive detection of arteriosclerosis is significant in preventing cardiovascular problems. In this study, the characterized parameters indicating the vascular stiffness were obtained by analyzing the electrocardiogram (ECG) and pulse wave signals, which can reflect the early change of vascular condition, and can predict the risk of cardiovascular diseases. Considering the coupling of ECG and pulse wave signals, and the association with atherosclerosis, we used the ECG signal characteristic parameters, including RR interval, QRS wave width and T wave amplitude, as well as the pulse wave signal characteristic parameters (the number of peaks, 20% main wave width, the main wave slope, pulse rate and the relative height of the three peaks), to evaluate the samples. We then built an assessment model of arteriosclerosis based on Adaptive Network-based Fuzzy Interference System (ANFIS) using the obtained forty sets samples data of ECG and pulse wave signals. The results showed that the model could noninvasively assess the arteriosclerosis by self-learning diagnosis based on expert experience, and the detection method could be further developed to a potential technique for evaluating the risk of cardiovascular diseases. The technique will facilitate the reduction of the morbidity and mortality of the cardiovascular diseases with the effective and prompt medical intervention.
The pulse amplitude of fingertip volume could be improved by selecting the vascular dense area and applying appropriate pressure above it. In view of this phenomenon, this paper used Comsol Multiphysics 5.6 (Comsol, Sweden), the finite element analysis software of multi-physical field coupling simulation, to establish the vascular tissue model of a single small artery in fingertips for simulation. Three dimensional Navier-Stokes equations were solved by finite element method, the velocity field and pressure distribution of blood were calculated, and the deformation of blood vessels and surrounding tissues was analyzed. Based on Lambert Beer's Law, the influence of the longitudinal compression displacement of the lateral light surface region and the tissue model on the light intensity signal is investigated. The results show that the light intensity signal amplitude could be increased and its peak value could be reduced by selecting the area with dense blood vessels. Applying deep pressure to the tissue increased the amplitude and peak of the signal. It is expected that the simulation results combined with the previous experimental experience could provide a feasible scheme for improving the quality of finger volume pulse signal.