(B) The signal upstream and downstream sign was determined based on the average frequency calculated from the power spectrum of the upward and downward parts of the filtered signal. A notch filter was used, which automatically identifies and reduces effects of the local electricity grid (e.g., 50/60 Hz noise) in the recording. In general, each EGM record went through (A) a prefiltering process to clear the data from environmental noise. However, tissues from different mammals differ in their beating rate, and thus, BRV parameters must be adjusted for different mammals ( Behar et al., 2018b).įigure 2 summarizes the steps used for beat detection in the EGM record, and Figure 3 shows a representative example of the analysis step on one representative signal. Furthermore, mice are practical as aging models due to their short lifespan ( Liu et al., 2014 Yaniv et al., 2016). On the other hand, mouse models are commonly used for overexpression or knockout of genes implicated in human cardiovascular diseases ( Thireau et al., 2008 Tzimas et al., 2017 Hook et al., 2018). The rabbit is the smallest mammal with intracellular Ca 2+ dynamics similar to humans ( Bers, 2002 Terentyev et al., 2014 Morrissey et al., 2017). (iii) Isolating pacemaker tissue from healthy human patients is rare consequently, other mammals are commonly used for cardiovascular research, with rabbits and mice being the most common mammal species used for such research. However, there is no standard method to derive BRV from HRV, and there are no publicly available programs to analyze pacemaker tissue BRV. (ii) Assuming that the first limitation is overcome, the beating rate of the tissue can be calculated from the EGM signals. To date, there is no database of mammalian EGM recordings available for the development of such a tool, and there are no standardized, state-of-the-art, partially or fully automated tools to analyze such recordings. Therefore, different analysis tools are required to determine the beating rate from SAN-isolated tissue EGM than those used to determine beating rates from whole-body ECG recordings. However, EGM signals differ in beat morphology and rate from in vivo ECG signals even if both are from the same mammal (see Figure 1). A number of limitations hinder such research: (i) The electrogram (EGM) is used to measure electrical signals recorded on the isolated tissue surface and reflects the inner currents in this tissue. However, although the beating rate of the SAN changes on a beat-to-beat basis, BRV has not been extensively explored in isolated SAN tissue. HRV has been quantified in vivo ( Goldberger et al., 2000 Behar et al., 2018b) and the beating rate variability (BRV, refers to variability measured under in situ or in vitro conditions) has been quantified in single pacemaker cells ( Zaza and Lombardi, 2001 Yaniv et al., 2011). The heart rate variability (HRV, refers to variability measured under in vivo conditions) has been suggested as a powerful tool to explore system function ( Burg et al., 1993 Bergfeldt and Haga, 2003 Rosenberg et al., 2020).
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While ECG recordings (i.e., in vivo) in a variety of mammals and electrical recordings of single pacemaker cells ( in vitro) are routinely performed in many labs, electrical data from isolated pacemaker tissue are limited.
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However, 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. When exploring the function of internal pacemaker mechanisms (see for example Yaniv et al., 2015 Behar et al., 2016), the in vitro conditions of isolated pacemaker cells is the optimal experimental model. To understand the role and relative contribution of each signal, experiments must be performed under in vivo, in situ, and in vitro conditions. 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). The normal heart beat dynamics involves orchestration of short- and long-scale periodic signals.