Structure of Poincare plots revealed by their graph analysis and low pass filtering of the RRI time series

Objectives: In order to reveal their structure, Poincare ' plots (PP) of electrocardiogram (ECG) RR intervals (RRI) were studied as linear edge planar directed graphs, obtained by connecting all their sequential points. We were also aimed at studying their graph complexity properties. Methods: RRI signals were subjected to a series of different window length (WL) Moving Average Low Pass (MALP) filters. For each filtered graph, four standard PP descriptors: Pearson's coefficient, SD1, SD2, and SD2/ SD1 were calculated, as well as four new graph complexity measures: mean angle between adjacent graph edges; mean number of edge crossings; directional complexity and directional entropy. This approach was applied to signals of twenty young healthy subjects, recorded in four experimental conditions - combination of two body postures (supine and standing) and two breathing regimes (spontaneous and slow 0.1 Hz). Results: We found that PP graphs consist of two superimposed components: one originating from Respiratory Sinus Arrhythmia (RSA) oscillations, the other from slow variations (SV) of the RRI time series. This result was further corroborated by observing the transformation of a PP cloud shape occurring in filtered graphs. When applied to subjects, the outcome was that three measures significantly differentiated the two breathing regimes in the RSA region of the WL domain, while four other measures were able to differentiate two body postures in the SV WL region. Discussion: After obtaining these results in healthy, we expect to successfully apply this approach to patients suffering from different pathological conditions.

Automated summary

This study analyzed Poincare plots of electrocardiogram RR intervals to reveal their structure and complexity properties. The results showed that these plots consist of components originating from respiratory sinus arrhythmia oscillations and slow variations of the RR interval time series. The method was able to differentiate body postures and breathing regimes in healthy subjects, suggesting its potential application in patients with different pathological conditions.