A Continuous Noninvasive Arterial Pressure (CNAP) Approach for Health 4.0 Systems

Health 4.0 can provide effective ways to improve the health status of subjects by taking advantage of cyber-physical systems and Internet of things technologies for the solution of healthcare problems. One of these is represented by suitably estimating blood pressure values of subjects in a continuous, real-time, and noninvasive way. To address it, we propose an approach only requiring a photoplethysmography (PPG) sensor and a mobile/desktop device. The approach avails itself of genetic programming to automatically find an explicit relationship between blood pressure values and PPG ones. This relationship is tested on a set of 11 subjects and compared against other regression methods, and turns out to be better. Namely, the root-mean-square error values are equal to 8.49 and 6.66 for the systolic and the diastolic blood-pressure values, respectively. Those for the relative error, instead, are equal to 5.55% for the systolic and 6.59% for the diastolic values.