Abstract
Traditional Poincaré plot analysis (PPA) represents a two-dimensional graphical and quantitative representation of a time series dynamics. However, traditional PPA indices measure mainly linear aspects of the heart rate variability (HRV).
Therefore, we introduced a new method of PPA - the segmented Poincaré plot analysis (SPPA) that retains essential nonlinear characteristics of the HRV and other time series.
Additional insights into the underlying physiological mechanisms have been gained by extending the methodology of SPPA. Thus we developed the lagged SPPA (LSPPA) that investigates time correlations of the BBI.
For the first time we could demonstrate that an HRV index from SPPA was able to contribute to risk stratification in patients suffering from DCM. LSPPA provides a prognostic preview for DCM patients regarding several associated symptoms such as endothelial dysfunctions and increased risk stratification in DCM to 92% accuracy.
SPPA was also applied to BBI time series and blood pressure signals to investigate the coupling between those two time series.
In several studies we could demonstrate that the applications of SPPA and LSPPA lead to much more information about impaired autonomic regulation and have the potential to be applied in much more fields of medical diagnosis and risk stratification.
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Khandoker, A.H., Karmakar, C., Brennan, M., Voss, A., Palaniswami, M. (2013). Segmented Poincaré Plot Analysis and Lagged Segmented Poincaré Plot Analysis. In: Poincaré Plot Methods for Heart Rate Variability Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-7375-6_6
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