Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization
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.
KeywordsComputer-aided diagnostics Heart rate variability analysis Ensemble learning Personalized medicine
- 4.Gavrishchaka, V., Senyukova, O., Davis, K.: Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics. In: Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol. 823, pp. 107–126. Springer International Publishing, Cham (2015)Google Scholar
- 6.Makowiec, D., Dudkowska, A., Zwierz, M., et al.: Scale invariant properties in heart rate signals. Acta Phys. Pol. B 37(5), 1627–1639 (2006)Google Scholar
- 9.Senyukova, O., Gavrishchaka, V.: Ensemble decomposition learning for optimal utilization of implicitly encoded knowledge in biomedical applications. In: IASTED International Conference on Computational Intelligence and Bioinformatics, pp. 69–73. ACTA Press, Calgary (2011)Google Scholar
- 10.Senyukova, O., Gavrishchaka, V., Koepke, M.: Universal multi-complexity measures for physiological state quantification in intelligent diagnostics and monitoring systems. In: Pham, T.D., Ichikawa, K., Oyama-Higa, M., Coomans, D., Jiang, X. (eds.) ACBIT 2013. CCIS, vol. 404, pp. 76–90. Springer, Heidelberg (2014)CrossRefGoogle Scholar
- 11.Task Force of the European Society of Cardiology, the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93, 1043–1065 (1996)Google Scholar