Genetic Programming for a Wearable Approach to Estimate Blood Pressure Embedded in a Mobile-Based Health System

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Sannino:2015:ieeeICTAI,
  author =       "Giovanna Sannino and Ivanoe {De Falco} and 
                 Giuseppe {De Pietro}",
  booktitle =    "27th IEEE International Conference on Tools with
                 Artificial Intelligence (ICTAI)",
  title =        "Genetic Programming for a Wearable Approach to
                 Estimate Blood Pressure Embedded in a Mobile-Based
                 Health System",
  year =         "2015",
  pages =        "775--783",
  abstract =     "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.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICTAI.2015.115",
  ISSN =         "1082-3409",
  month =        nov,
  notes =        "Inst. for High-Performance Comput. & Networking,
                 Naples, Italy

                 Also known as \cite{7372211}",
}

Genetic Programming entries for Giovanna Sannino Ivanoe De Falco Giuseppe De Pietro

Citations