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.
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Senyukova, O., Gavrishchaka, V., Sasonko, M., Gurfinkel, Y., Gorokhova, S., Antsygin, N. (2016). Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_18
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