On-line supplement
Synchronization of sinoatrial node pacemaker cell clocks and its autonomic modulation impart complexity to heart beating intervals
Short title: Beating-rate variability of sinoatrial node cells
Yael Yaniv1,2*, PhD; Ismayil Ahmet1, MD PhD; Jie Liu1,3, MD PhD; Alexey E. Lyashkov4, PhD; Toni-Rose Guiriba1; Yosuke Okamoto1, MD PhD; Bruce D. Ziman1, MS;
and Edward G. Lakatta1*, MD
1Laboratory of Cardiovascular Science, Biomedical Research Center, Intramural Research Program, National Institute on Aging, NIH, Baltimore, Maryland, USA.2Biomedical Engineering Faculty, Technion-IIT, Haifa, Israel. 3Cardiovascular Physiology Laboratory, School of Medical Sciences, F13-Anderson Stuart Building, University of Sydney, Sydney NSW 2006, Australia. 4Department of Molecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, Maryland, USA.
*Corresponding authors:
Yael Yaniv, PhD
Biomedical Engineering Faculty
Technion-IIT, Haifa, Israel
Email:
Edward G. Lakatta, MD
Laboratory of Cardiovascular Science, Biomedical Research Center, Intramural Research
Program, National Institute on Aging, NIH, Baltimore, Maryland, USA
Email:
Linear time-domain methods for heart rate or beating rate variability
All measurements were performed after the beating interval in each of the four conditions reached a steady state.
SDNN
Standard deviation of all beating interval lengths (BIL). 1
is the arithmetic mean of the values of BIL.
RMSDD
Root mean square of successive BIL.1
(1)
where N is the total number of beating intervals.
CV
Coefficient of variation is a normalized measure of dispersion of a probability distribution.
(2)
pNN50
Percentage of adjacent beating intervals differing by more than 50 ms.1
(3)
where N is the total number of BI.
Frequency-domain methods for heart rate or beating rate variability
Four raw frequency-domain statistics were extractedfrom each Fourier spectrum (2048 beats under in vivo conditions and in the intact SAN, and 1024 beats in single SANC); the estimated power in each of three bands was calculated: very-low frequency (VLF), low frequency (LF), and high frequency (HF) and the total power (Total) over all three bands. Due to different average beating rates in different fundamental states (i.e., in vivo, isolated SAN, single SANC) three different frequency regimes were defined: the VLF embodied frequency below 4% of the average beating rate, the LF domain included frequencies between VLF and 15% of the average beating rate, and the HF embodied frequencies between LF and 40% of the average beating rate. Note that these frequency domains are similar to those defined in humans.2In vivo, VLF was below 0.19 Hz, LF was between 0.19 to 0.72 Hz and HF was between 0.72 to 1.92 Hz. In the isolated intact SAN VLF was below 0.11 Hz, LF was between 0.11 to 0.41 Hz and HF was between 0.41 and 1.1 Hz. In isolated SANC VLF was below 0.09 Hz, LF was between 0.09 to 0.36 Hz and HF was between 0.36 and 0.96 Hz.
Poincaré plot
The Poincaré plotvisualizes each beat interval as a function of the preceding interval in order to display the correlation between consecutive intervals.
Power-Law analysis
Fourier analysis was performed on the heart-beat intervals (see above). A robust line-fitting algorithm of log power spectrum density versus log frequency at VLF regime was applied. The slope of the line is the fractal scale exponent, β. A power law was assumed to be present if R2>0.7.3
Detrended Fluctuation Analysis
Detrended fluctuation analysis (DFA) quantifies the degree of correlation among time scales embedded within the heart beat intervals.4 The root mean square fluctuations of the integrated and deternded data time series (F(n)) were calculated in two windows: short-term DFA, in a window size between 4 and 16 beats and the long-term DFA in a window size between 16 and 64 beats. A robust line-fitting algorithm of log fluctuation versus log window size was applied to the short and long-term DFA windows.These bi-fractal slopes are described by short- and long-term exponents, α1 and α2, respectively.
Approximate entropy (ApEn)
Approximate entropy, a measure quantifying the regulatory of the time series, was calculated from the average values of segments encompassing 1500 beats with fixed input variables of m=2 and r=20% as previously described.5
References
1.Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 1996;17:354-381.
2.Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996;93:1043-1065.
3.Kobayashi M, Musha T. 1/f fluctuation of heartbeat period. IEEE Trans Biomed Eng 1982;29:456-457.
4.Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:E215-220.
5.Pincus SM, Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol 1994;266:H1643-1656.
Fig. S1: ECG recording in vivo, electrogram and contraction in single pacemaker cell. Spontaneous beats were recorded in single pacemaker cells using a myocyte contractility recording system (IonOptix, MA). The contraction peak was identified in each beat (by analyzing the first derivative of the contraction pattern). The distance between successive contraction peaks was defined as cycle length.
Fig. S2:(A)Simultaneousrecordings of electrical activity and contraction withinthe same pacemaker cell. (B) The interval between successive APs is similar to the distance between successive contractions. (C) The excitation-contraction delay remains constant and independent of the beating interval and (D) the excitation-contraction delay is not correlated with the previous beat interval.
Fig. S3: Representative examples of power spectrum density (PSD) at different levelsof integration from the heart in vivo to single isolated pacemaker cells.
Fig. S4: Spectral analysis of single pacemaker cell that did not exhibit fractal-like behavior (left panel) and one that did (right panel).
Figure S1.
FigureS2.
FigureS3.
FigureS4.
1