What is HeartTrends?

In clinical medicine, the dynamics of the beat-to-beat (RR) time series is commonly represented by a phase-space (or Poincaré) plot, where each RR interval is plotted against the previous one. The classification of the phase-space plot is traditionally performed by visual inspection and semi-quantitative analysis describing the features of the plot, as length or width, but that approach ignores the varying density of points leading to similar plots due to hearts with very different dynamics.

The Multipole MPW analysis is a relatively new way of investigating the Poincaré plot from complex time series. We interpret the Poincaré plot as a two-dimensional body, where each data point in the plot is assigned a unit mass, in order to describe the total mass distribution within the plot. The measures obtained from this kind of analysis bear intrinsic time dependence due to the very construction of the plot. As a result the Multipole MPW method derives information from the time, amplitude, and frequency domains as well as reflecting increased randomness in the RR interval time series. Traditional HRV-measures derive only information from one domain, which is the reason that The Multipole MPW Method shows more prognostic power than previous suggested risk markers.

From the time series one may calculate the leading multipoles: the quadrupoles, octoupoles, and the hexadecapoles, and from which the new HRV parameter MPW is derived. The Quadrupole (Qyy), for example, describes the overall distribution of data points in the Poincaré Plot (i.e., the shape of the plot). It was found to be a strong predictor of mortality in a population of post-Myocardial Infarction (MI) patients with both a depressed and preserved Left Ventricular Ejection Fraction (LVEF) [1]. When used in combination with MPW as a weighted multipole parameter, it has been shown to be a stronger predictor of mortality after MI than SDNN and the short-term scaling exponent Alpha-1 [2].

The varying density of data points implies that some other measures based on analysis of the plot incorrectly add the same significance to low populated areas of the plot as to higher populated areas. This is, for example, the case for SD12 which is the ratio between the length (SD2) and the width (SD1) of an imaginary ellipse fitted to the Poincaré Plot with the center in the average RR interval. In contrast to SD12, MPW is a relative density measure obtained from the plot with prevalence of the densest populated area.

Joergensen et al. [1] compared the Multipole method with the traditional HRV measures in the Nordic ICD study. Patients with AMI were screened with 2D Echocardiography and 24h Holter-recordings 2-14 days post-MI. Reduced MPW predicted both all-cause, cardiovascular mortality and sudden cardiovascular death in univariate Cox proportional hazard analysis.

In multivariate analysis with correction for known risk factors, MPW continued to show independent predictive value with a hazard value of 2.1 (C.L. 1.1-4.2), whereas none of the traditional HRV measures reached statistical significance. The assumption is that, due to MPW obtaining information from time as well as frequency intervals combined with focusing on only dense populated areas of the recurrence plot, prognostic power for VT/VF arrhythmias is enhanced relative to traditional HRV measures which do not receive information from the time domain.

[1] Joergensen RM, et al. Prediction of ventricular tachycardia in ICD patients based on the multipole method. European Heart Journal 2007, 28:414 (Abstract Supplement).

[2] Olesen RM et al. Statistical Analysis of Diamond MI Study by the Multipole Method. Physiol. Meas. 2005, 26:591-598.