Affiliation:
1. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, P. R. China
2. Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin 300130, P. R. China
Abstract
The aim of this study was to find a non-invasive pulse wave waveform index that was highly correlated with cardiovascular disease and to establish an effective model for cardiovascular health assessment in middle-aged men to provide early warning of possible cardiovascular and cerebrovascular diseases. Considering the characteristics of pulse wave easy to detect and rich physiological information, the paper collected pulse waves of healthy males at six age groups, and collected 50 samples per age group. The pulse wave waveform parameters of each sample were extracted, including inflow time, outflow time, total beat time, fast inflow time, inflow time ratio and waveform coefficient [Formula: see text] value, the differences of which in different age groups were analyzed. Stepwise regression analysis was used to establish the relationship between age and pulse waveform parameters. The results show that the indicators of inflow time, inflow time ratio and fast inflow time have obvious differences with age, and with the increase of age, these three indicators show a steady upward trend. The indicators of outflow time, total beat time, and waveform coefficient are not sensitive to age changes. A predictive model of vessel age was established: [Formula: see text] time ([Formula: see text]). The pulse wave inflow time of hypertensive patients was substituted into the above-mentioned model, and the calculated blood vessel age was greater than the actual age. The age difference is greater than 5 years old. This study suggests that the pulse wave parameters of inflow time, fast inflow time and inflow time ratio has a significant and stable trend with age, indicating that they are closely related to vascular elasticity, compliance and stiffness, and can be used as predictors of cardiovascular disease.
Funder
Natural Science Foundation of Tianjin
CAMS Innovation Fund for Medical Science
Publisher
World Scientific Pub Co Pte Lt
Cited by
2 articles.
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