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Purpose: The objective of the study was to identify an explicit model to indirectly monitor blood pressure by using the electrocardiography and heart rate variability parameters, and the plethysmography. Those latter signals can be monitored through wearable non-invasive sensors, namely an electrocardiography sensor and a finger pulse oximeter sensor. The developed model was included in a real-time mobile monitoring system to realize a wearable continuous non-invasive arterial pressure monitor. Context: Continuous non-invasive arterial pressure methods can be used to continuously measure arterial blood pressure in real time and without any need for patient's body cannulation. Currently there is a high request for accurate and easy-to-use continuous non-invasive arterial pressure systems. Consequently, an increasing focus on these devices exists. Several non-invasive approaches to blood pressure have been attempted, some of which are described in [1]. Methods: The explicit model was developed under the form of a function by combining heart rate variability parameters and plethysmography measurements. We decided to avail ourselves of a Genetic Programming technique for this regression problem because it can automatically find an explicit model for the relationship between the independent variables and a dependent one, in this case one between the systolic and the diastolic blood pressure values. Therefore, once hypothesized the existence of a nonlinear relationship between heart activity, and thus electrocardiography and heart rate variability parameters, plethysmography and blood pressure values, and chosen a fitness function, we found, from among the huge number of possible models, the one that best describes the fundamental features of this relationship.

About the discovery of an explicit model to monitor blood pressure in a non-invasive way