ICSH 2015: Smart Health pp 118-130 | Cite as

A Game Theoretic Predictive Modeling Approach to Reduction of False Alarm

  • Fatemeh Afghah
  • Abolfazl Razi
  • S. M. Reza Soroushmehr
  • Somayeh Molaei
  • Hamid Ghanbari
  • Kayvan Najarian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9545)


False alarm is one of the main concerns in intensive care units which could result in care disruption, sleep deprivation, insensitivity of care–givers to alarms and so on. Many approaches such as improving the quality of physiological signals by filtering and developing more accurate sensors have been proposed in the last two decades to suppress the rate of false alarm. Moreover, some multi–parameter/feature methods have been developed to classify the alarms more accurately. One of the main problems facing these methods is that they neglect those features that individually have low impact on the accuracy. In this paper, we propose a model based on coalition game that considers the inter–features mutual information which results in gaining the accuracy of the classification. Simulation results on a database produced by four hospitals shows the superior performance of the proposed method compared to other existing methods.


False alarm Feature selection Coalition game theory Classification 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fatemeh Afghah
    • 1
  • Abolfazl Razi
    • 1
  • S. M. Reza Soroushmehr
    • 2
    • 3
  • Somayeh Molaei
    • 2
    • 3
  • Hamid Ghanbari
    • 4
  • Kayvan Najarian
    • 2
    • 3
    • 5
  1. 1.Electrical Engineering and Computer Science DepartmentNorthern Arizona UniversityFlagstaffUSA
  2. 2.Department of Emergency MedicineUniversity of MichiganAnn ArborUSA
  3. 3.Michigan Center for Integrative Research in Critical Care: MCIRCCUniversity of MichiganAnn ArborUSA
  4. 4.Internal MedicineUniversity of MichiganAnn ArborUSA
  5. 5.Computational Medicine and Bioinformatics DepartmentUniversity of MichiganAnn ArborUSA

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