Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization

  • Olga Senyukova
  • Valeriy Gavrishchaka
  • Maria Sasonko
  • Yuri Gurfinkel
  • Svetlana Gorokhova
  • Nikolay Antsygin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)

Abstract

Accurate and timely diagnostics does not warranty successful treatment outcome due to subtle personal differences, especially in the case of complex or rare cardiac abnormalities. A proper representation of global cardio dynamics could be used for quick and objective matching of the current patient to former cases with known treatment plans and outcomes. Previously we have proposed the approach for heart rate variability (HRV) analysis based on ensembles of different measures discovered by boosting algorithms. Unlike original HRV techniques, ensemble-based metrics could be much more accurate in early detection of short-lived or emerging abnormal regimes and slow changes in long-range dynamic patterns. Here we demonstrate that the same metrics applied to long HRV time series, collected by Holter monitors or other means, could provide effective characterization of global cardiovascular dynamics for decision support in discovery and optimization of personalized treatments.

Keywords

Computer-aided diagnostics Heart rate variability analysis Ensemble learning Personalized medicine 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Olga Senyukova
    • 1
  • Valeriy Gavrishchaka
    • 2
  • Maria Sasonko
    • 3
  • Yuri Gurfinkel
    • 3
  • Svetlana Gorokhova
    • 3
  • Nikolay Antsygin
    • 4
  1. 1.Faculty CMCLomonosov Moscow State UniversityMoscowRussian Federation
  2. 2.Department of PhysicsWest Virginia UniversityMorgantownUSA
  3. 3.Research Clinical Center of JSC Russian RailwaysMoscowRussian Federation
  4. 4.Children’s City Hospital #1St. PetersburgRussian Federation

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