Indirect Blood Pressure Evaluation by Means of Genetic Programming

This paper relies on the hypothesis of the existence of a nonlinear relationship between Electrocardiography (ECG) and Heart Related Variability (HRV) parameters, plethysmography (PPG), and blood pressure (BP) values. This hypothesis implies that, rather than continuously measuring the patient’s BP, both their systolic and diastolic BP values can be indirectly measured as follows: a wearable wireless PPG sensor is applied to a patient’s finger, an ECG sensor to their chest, HRV parameter values are computed, and regression is performed on the achieved values of these parameters. Genetic Programming (GP) is a Computational Intelligence paradigm that can at the same time automatically evolve the structure of a mathematical model and select from among a wide parameter set the most important parameters contained in the model. Consequently, it can carry out very well the task of regression. The scientific literature of this field reveals that nobody has ever used GP aiming at relating parameters derived from HRV analysis and PPG to BP values. Therefore, in this paper we have carried out preliminary experiments on the use of GP in facing this regression task. GP has been able to find a mathematical model expressing a nonlinear relationship between heart activity, and thus ECG and HRV parameters, PPG and BP values. The experimental results reveal that the approximation error involved by the use of this method is lower than 2 mmHg for both systolic and diastolic BP values.