Distortion properties of the interval spectrum of IPFM generated heartbeats for heart rate variability analysis

Poincaré Plot Image and Rhythm-Specific Atlas for Atrial Bigeminy and Atrial Fibrillation Detection

A detector based only on RR intervals capable of classifying other tachyarrhythmias in addition to atrial fibrillation (AF) could improve cardiac monitoring. In this paper a new classification method based in a 2D non-linear RRI dynamics representation is presented. For this aim, the concepts of Poincaré Images and Atlases are introduced. Three cardiac rhythms were targeted: Normal sinus rhythm (NSR), AF and atrial bigeminy (AB). Three Physionet open source databases were used. Poincaré Images were generated for all signals using different Poincaré plot configurations: RR, dRR and RRdRR. The study was computed for different time window lengths and bin sizes. For each rhythm, the Poincaré Images of the 80% of that rhythm's patients were used to create a reference image, a Poincaré Atlas. The remaining 20% were used as test set and classified into one of the three rhythms using normalized mutual information and 2D correlation. The process was iterated in a tenfold cross-validation and patient-wise dataset division. Sensitivity results obtained for RRdRR configuration and bin size 40 ms, for a 60 s time window were 94.35% ±3.68, 82.07% ±9.18 and 88.86% ±12.79 with a specificity of 85.52% ±7.46, 95.91% ±3.14, 96.10% ±2.25 for AF, NSR and AB respectively. Results suggest that a rhythms general RRI pattern may be captured using Poincaré Atlases and that these can be used to classify other signal segments using Poincaré Images. In contrast with other studies, the former method could be generalized to more cardiac rhythms and does not depend on rhythm-specific thresholds.

Multi-scale Tone Entropy in differentiating physiologic and synthetic RR time series

Heart rhythm is extrinsically modulated by the autonomic nervous system and recently, the Tone-Entropy (TE) measurement was reported as a measure of autonomic balance and activity in time domain HRV analysis. Current algorithm for T-E measurement describes only beat-to-beat or influence of a heart beat on a train of succeeding beats on a single scale. Therefore, conventional T-E analysis has often not been able to discern various physiological conditions using heart rate variability (HRV) signal. In this study, we will present a mathematical framework to define multi-scale T-E analysis, apply this in differentiating physiological and synthetic RR time series. Finally, we compare the performance of proposed parameters with conventional T-E measurements.

The integral pulse frequency modulation model with time-varying threshold: application to heart rate variability analysis during exercise stress testing

In this paper, an approach for heart rate variability analysis during exercise stress testing is proposed based on the integral pulse frequency modulation (IPFM) model, where a time-varying threshold is included to account for the nonstationary mean heart rate. The proposed technique allows the estimation of the autonomic nervous system (ANS) modulating signal using the methods derived for the IPFM model with constant threshold plus a correction, which is shown to be needed to take into account the time-varying mean heart rate. On simulations, this technique allows the estimation of the ANS modulation on the heart from the beat occurrence time series with lower errors than the IPFM model with constant threshold (1.1% ± 1.3% versus 15.0% ± 14.9%). On an exercise stress testing database, the ANS modulation estimated by the proposed technique is closer to physiology than that obtained from the IPFM model with constant threshold, which tends to overestimate the ANS modulation during the recovery and underestimate it during the initial rest.

Characterizing nonlinear heartbeat dynamics within a point process framework

Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.

Time-Varying Analysis Methods and Models for the Respiratory and Cardiac System Coupling in Graded Exercise

The analysis of heart period series is a difficult task especially under graded exercise conditions. From all the information present in these series, we are the most interested in the coupling between respiratory and cardiac systems, known as respiratory sinus arrythmia. In this paper, we show that precise patterns concerning the respiratory frequency can be extracted from the heart period series. An evolutive model is introduced in order to achieve tracking of the main respiratory-related frequencies and their time-varying amplitudes. Since respiration acts to modulate the sinus rhythm, we relate the frequencies and amplitudes to this modulation by analyzing in detail its nonlinear transformation giving the heart period signal. This analysis is performed assuming stationary conditions but also in the realistic case where the mean heart period, the amplitude, and the frequency of the respiration are time-varying. Since this paper is devoted to the theoretical and complete presentation of the method used in a physiological study published elsewhere, the capabilities of our method will be illustrated in a realistic simulated case.

Do the high-frequency indexes of HRV provide a faithful assessment of cardiac vagal tone? A critical theoretical evaluation

When spectral analysis of the heart rate (HR) signal is performed, it is quite common to attribute the HF indexes of heart rate variability (HRV) to cardiac vagal control. The paradigm underlying this attribution states that changes in cardiac vagal outflow correspond to a proportional change in respiratory sinus arrhythmia (RSA). However, recent studies have demonstrated that variations in these indexes do not necessarily reflect proportional changes in vagal tone. The current study provides a theoretical evaluation of the relationship between mean HR, RSA, and cardiac vagal tone. This evaluation is based on a theoretical model, which quantifies the differential effects of vagal blockade by a competitive muscarinic antagonist on the HF indexes of HRV. The model relies on several assumptions that reflect the basic physiology of the sinoatrial (SA) node, as well as pharmacological relations that describe agonist/antagonist equilibrium at the SA receptors. The mathematical framework of this model is the integral-pulse-frequency-modulation (IPFM) process, and its derivations lead to a specific expressions for the dependence of HF and mean HR on the level of vagal blockade. These expressions provide a new insight into the relationship between mean HR, RSA, and vagal tone, and explain conflicting experimental results previously published.