Systolic blood pressure estimation method using electrocardiogram RRI data.

Having obtained an idea from the Guyton model appertaining to the arterial pressure control mechanisms, we propose a novel chaos time series analysis method working with the non-nervous intermediate pressure control mechanism. Responses of the intermediate pressure control mechanisms are obtained from an electrocardiogram RRI delay coordinate system, and two frequency ranges are determined via residual functions to identify action and compensation by using the two best approximation functions. Absolute values of objects for control are estimated with the gradients of tangent and quantities of state at the inflection points of the best approximation functions. We obtained a polynomial determining a three-dimensional response surface (R2 = 0.814) that converted the two gradients of tangent calculated from the electrocardiogram RRI data of 225 cases and brachial systolic blood pressure into quantities of state. Further, the estimated values obtained by inputting the electrocardiogram RRI data of 120 readings from one subject into this polynomial showed strong correlation (R2 = 0.8564) with the measured brachial systolic blood pressure. Thus, it showed that the gradients of tangent were parameters grasping chaotic variation of the intermediate pressure control mechanisms.