Abstract

BackgroundBecause of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under in vivo and in situ conditions. The in situ approach is valuable when exploring the mechanisms that govern the beating rate and rhythm of the sinoatrial node (SAN), the heart’s primary pacemaker. SAN beating rate changes on a beat-to-beat basis. However, to date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings.MethodsWe used EGM recordings obtained from control SAN tissues of rabbits (n = 9) and mice (n = 30) and from mouse SAN tissues (n = 6) that were exposed to drug intervention. The data were harnessed to develop a beat detector to derive the beat-to-beat interval time series from EGM recordings. We adapted BRV measures from heart rate variability and reported their range for rabbit and mouse.ResultsThe beat detector algorithm performed with 99% accuracy, sensitivity, and positive predictive value on the test (mouse) and validation (rabbit and mouse) sets. Differences in the frequency band cutoff were found between BRV of SAN tissue vs. heart rate variability (HRV) of in vivo recordings. A significant reduction in power spectrum density existed in the high frequency band, and a relative increase was seen in the low and very low frequency bands. In isolated SAN, the larger animal had a slower beating rate but with lower BRV, which contrasted the phenomena reported for in vivo analysis. Thus, the non-linear inverse relationship between the average HR and HRV is not maintained under in situ conditions. The beat detector, BRV measures, and databases were contributed to the open-source PhysioZoo software (available at: https://physiozoo.com/).ConclusionOur approach will enable standardization and reproducibility of BRV analysis in mammals. Different trends were found between beating rate and BRV or HRV in isolated SAN tissue vs. recordings collected under in vivo conditions, respectively, implying a complex interaction between the SAN and the autonomic nervous system in determining HRV in vivo.

Highlights

  • The normal heart beat dynamics involves orchestration of shortand long-scale periodic signals

  • Two detrended fluctuations analysis (DFA) coefficients were reported for the slopes before and after 16 beats. We evaluated this cutoff for beating rate variability (BRV) analysis

  • We present the results of the performance of the beat detector on mouse and rabbit sinoatrial node (SAN) tissues recordings collected under basal conditions, the range of BRV measures obtained for mouse and rabbit SAN tissues, the interpretation of BRV results in comparison to heart rate variability (HRV), and an example of insights on pacemaker function gained from drug response BRV analysis

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Summary

Introduction

The normal heart beat dynamics involves orchestration of shortand long-scale periodic signals. These signals are generated by opening and closing of membranal channels (Adair, 2003) in heart pacemaker cells, interaction between pacemaker cells (Michaels et al, 1986), and the pacemaker cell interaction with other body systems (Yang and Xu-Friedman, 2013). When exploring the interconnected pacemaker cell mechanisms, the in situ environment of isolated sinoatrial node (SAN) tissue isolating it from all environmental effects (hormonal or nervous system) is the ideal model. Because of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under in vivo and in situ conditions. To date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings

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