Recent modeling advances have successfully derived time-varying estimates of nonlinear heartbeat dynamics, whose quantifiers mainly rely on first-order moments (i.e., average over time). While, these metrics account for the information carried by the tonic (slow trend) nonlinear dynamics, they fail to quantify potentially meaningful information nested in the superimposed phasic (high-frequency) activity of the physiological series. In this study, we investigate new metrics from phasic activity of time-varying bispectral indexes, which are derived from nonlinear point-process modeling of heartbeat dynamics. Instantaneous phasic activity is derived using wavelet decomposition of time-varying bispectral power, and quantified using the area under the curve (AUC) and variance (VAR) metrics. Results, gathered from ECG series from 22 healthy volunteers undergoing cold-pressor test (CPT), show that phasic components of low-frequency (LL) instantaneous bispectra significantly change between resting and CPT states, as quantified by AUC and VAR. In conclusion, phasic activations of bispectral estimates carry meaningful information for the nonlinear assessment of sympatho-vagal regulation onto the heart. This study poses a foundation for a novel signal processing framework investigating time-varying estimates of nonlinear cardiovascular control.
Investigating Phasic Activity of Time-Varying High-Order Spectra: A Heartbeat Dynamics Study during Cold-Pressor Test
Barbieri R.;
2018-01-01
Abstract
Recent modeling advances have successfully derived time-varying estimates of nonlinear heartbeat dynamics, whose quantifiers mainly rely on first-order moments (i.e., average over time). While, these metrics account for the information carried by the tonic (slow trend) nonlinear dynamics, they fail to quantify potentially meaningful information nested in the superimposed phasic (high-frequency) activity of the physiological series. In this study, we investigate new metrics from phasic activity of time-varying bispectral indexes, which are derived from nonlinear point-process modeling of heartbeat dynamics. Instantaneous phasic activity is derived using wavelet decomposition of time-varying bispectral power, and quantified using the area under the curve (AUC) and variance (VAR) metrics. Results, gathered from ECG series from 22 healthy volunteers undergoing cold-pressor test (CPT), show that phasic components of low-frequency (LL) instantaneous bispectra significantly change between resting and CPT states, as quantified by AUC and VAR. In conclusion, phasic activations of bispectral estimates carry meaningful information for the nonlinear assessment of sympatho-vagal regulation onto the heart. This study poses a foundation for a novel signal processing framework investigating time-varying estimates of nonlinear cardiovascular control.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
