An Approach for Prediction of Acute Hypotensive Episodes via the Hilbert-Huang Transform and Multiple Genetic Programming Classifier

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{journals/ijdsn/JiangLHF15,
  author =       "Dazhi Jiang and Liyu Li and Bo Hu and Zhun Fan",
  title =        "An Approach for Prediction of Acute Hypotensive
                 Episodes via the Hilbert-Huang Transform and Multiple
                 Genetic Programming Classifier",
  journal =      "International Journal of Distributed Sensor Networks",
  year =         "2015",
  volume =       "11",
  number =       "8",
  pages =        "354807:1--354807:11",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1155/2015/354807",
  abstract =     "Acute hypotensive episodes (AHEs) are one of the
                 hemodynamic instabilities with high mortality rate that
                 is frequent among many groups of patients. This study
                 presents a methodology to predict AHE for ICU patients
                 based on big data time series. The experimental data we
                 used is mean arterial pressure (MAP), which is
                 transformed from arterial blood pressure (ABP) data.
                 Then, the Hilbert-Huang transform method was used to
                 calculate patient's MAP time series and some features,
                 which are the bandwidth of the amplitude modulation,
                 the frequency modulation, and the power of intrinsic
                 mode function (IMF), were extracted. Finally, the
                 multiple genetic programming (Multi-GP) is used to
                 build the classification models for detection of AHE.
                 The methodology is applied in the datasets of the 10th
                 PhysioNet and Computers Cardiology Challenge in 2009
                 and Multiparameter Intelligent Monitoring for Intensive
                 Care (MIMIC-II). We achieve the accuracy of
                 83.33percent in the training set and 91.89percent in
                 the testing set of the 2009 challenge's dataset and the
                 84.13percent in the training set and 82.41percent in
                 the testing set of the MIMIC-II dataset.",
}

Genetic Programming entries for Dazhi Jiang Liyu Li Bo Hu Zhun Fan

Citations