Feasibility of cuff-free measurement of systolic and diastolic arterial blood pressure.

We validated a prototype cuff-free device for noninvasive estimation of blood pressure (BP). The system assumed a linear relation between BP values and the inverse of arterial blood pulse transit time, measured as time interval between the R wave on the electrocardiograph and the onset of the peripheral pulse wave on a finger plethysmogram. Thirty-three healthy subjects were analyzed at rest and during increasing stress exercise. To estimate subject-specific linear model parameters, the system was calibrated ad personam with reference to BP measures obtained by a cuff sphygmomanometer. High correlation values (R(2) = 0.89 and 0.78 for systolic and diastolic BP, respectively) and differences consistent with clinical requirements (mean discrepancy of -0.058 and -0.25 mm Hg; 95% confidence interval of -13.0 to +12.9 mm Hg and -11.3 to +10.8 mm Hg, for systolic and diastolic BP, respectively) were observed between device and reference measurements. Calibration parameter stability and accuracy level were confirmed in a midterm evaluation, 30 days after calibration. These findings suggest the suitability of the device for noninvasive BP monitoring and its potentiality for clinical applications. Improvements can be achieved by further investigation of the calibration procedure and sensor placement.

Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series.

A method to evaluate the direction and strength of causal interactions in bivariate cardiovascular and cardiorespiratory series is presented. The method is based on quantifying self and mixed predictability of the two series using nearest-neighbour local linear approximation. It returns two causal coupling indexes measuring the relative improvement in predictability along direct and reverse directions, and a directionality index indicating the preferential direction of interaction. The method was implemented through a cross-validation approach that allowed quantification of directionality without constraining the embedding of the series, and fully exploited the available data to maximise the prediction accuracy. Validation on short simulated bivariate time series demonstrated the ability of the method to capture different degrees of unidirectional and bidirectional interaction. Moreover, application to representative examples of heart rate, systolic arterial pressure and respiration series allowed the inference of causal relationships related to known physiological mechanisms and experimental conditions.