Indirect Blood Pressure Evaluation by Means of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Sannino:2015:BIOSTEC,
  author =       "Giovanna Sannino and Ivanoe {De Falco} and 
                 Giuseppe {De Pietro}",
  title =        "Indirect Blood Pressure Evaluation by Means of Genetic
                 Programming",
  booktitle =    "8th International Joint Conference on Biomedical
                 Engineering Systems and Technologies, BIOSTEC 2015",
  year =         "2015",
  editor =       "Alberto {Cliquet Jr.} and Ana L. N. Fred and 
                 Hugo Gamboa and Dirk Elias",
  pages =        "75--92",
  address =      "Lisbon, Portugal",
  month =        jan # " 12-15",
  publisher =    "Springer/SciTePress",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-27707-3",
  isbn13 =       "978-989-758-071-0",
  DOI =          "doi:10.1007/978-3-319-27707-3_6",
  abstract =     "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.",
  notes =        "DBLP
                 http://dblp.uni-trier.de/db/conf/biostec/biodevices2015.html#SanninoFP15
                 gives reference \cite{conf/biostec/SanninoFP15} as
                 BIODEVICES 2015 - Proceedings of the International
                 Conference on Biomedical Electronics and Devices,
                 Lisbon, Portugal, 12-15 January, 2015, pages 241--249
                 publisher by SciTePress",
}

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

Citations