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

Authors :
Kalauzi, Aleksandar
Matić, Zoran
Bojić, Tijana
Platiša, Mirjana M.
Kalauzi, Aleksandar
Matić, Zoran
Bojić, Tijana
Platiša, Mirjana M.
Source :
Biomedical Signal Processing and Control
Publication Year :
2023

Abstract

ObjectivesIn order to reveal their structure, Poincaré 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.MethodsRRI 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).ResultsWe 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.DiscussionAfter obtaining these results in healthy, we expect to successfully apply this approach to patients suffering from different pathological conditions.

Details

Database :
OAIster
Journal :
Biomedical Signal Processing and Control
Notes :
Biomedical Signal Processing and Control
Publication Type :
Electronic Resource
Accession number :
edsoai.on1363261436
Document Type :
Electronic Resource