Genetic Programming for a Wearable Approach to Estimate Blood Pressure Embedded in a Mobile-Based Health System
2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) (2015)
Vietri sul Mare, Italy
Nov. 9, 2015 to Nov. 11, 2015
Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypothesis of existence of a nonlinear relationship between plethysmography (PPG) and BP values has been investigated in this paper. If this hypothesis is true, then it is possible to indirectly measure patient's BP in a non-invasive way through the application of a wearable wireless PPG sensor to patient's finger and through the use of the results of a regression analysis aimed at linking PPG and BP values. To find the relationship between these two biomedical characteristics we have used here Genetic Programming (GP), because in a regression task it can evolve in an automatic way the structure of the most suitable explicit mathematical model. An analysis of the related scientific literature shows that this is the first attempt to mathematically relate PPG and BP values through GP. In this paper some preliminary experiments on the use of GP in facing this regression task have been carried out. As a result, for both systolic and diastolic BP values explicit mathematical models providing nonlinear relationship between PPG and BP values have been achieved, involving an approximation error of around 2 mmHg in both cases. A prototypal mobile-based system has been realized which is able to continuously estimate in real time the two BP values for any given patient by using only a plethysmography signal and the obtained mathematical models.
Blood pressure, Biomedical monitoring, Mathematical model, Genetic programming, Pressure measurement, Plethysmography, Fingers
G. Sannino, I. De Falco and G. De Pietro, "Genetic Programming for a Wearable Approach to Estimate Blood Pressure Embedded in a Mobile-Based Health System," 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, Italy, 2015, pp. 775-783.