Behavioral-Independent Features of Complex Heartbeat Dynamics

Abstract

We test whether the complexity of cardiac interbeat interval time series is simply a consequence of the wide range of scales characterizing human behavior, especially physical activity, by analyzing data taken from healthy adult subjects under three conditions with controls: (i) a "constant routine" protocol where physical activity and postural changes are kept to a minimum, (ii) sympathetic blockade, and (iii) parasympathetic blockade. We find that when fluctuations in physical activity and other behavioral modifiers are minimized, a remarkable level of complexity of heartbeat dynamics remains, while for neuroautonomic blockade the multifractal complexity decreases.

Phase Transition in a Healthy Human Heart Rate

“…Characteristic features at a critical point of a second order phase transition are the divergence of the relaxation time with strongly correlated fluctuations and the scale invariance in the statistical properties. Indeed, a healthy human heart rate has been confirmed robustly to show these types of behavior [1,5,7,11,12].Here we seek a phase transition phenomenon in heart rate in order to support the criticality hypothesis more strongly. We provide evidence that a clear transition of heart rate dynamics between distinct, stable ''phases'' can be revealed in heart rate fluctuations in healthy individuals.…”

Long-range correlations in heart rate variability during computer-mouse work under time pressure

“…The correlation between spectral components and scaling factors was weak under uncontrolled conditions [29]. Although the physiologic foundation of the correlation between the anomalies of scaling exponents and the risk of CVDs has not been well established, the scaling properties of HRV series has shown to be dependent on autonomic nervous activities [13] instead of behavior factors [14,15].…”

“…Some evidences had demonstrated that modifying the relative importance of sympathetic and parasympathetic nervous systems could lead to changes of the long-range correlations embedded in HRV series [13]. Moreover, scientists found that the long-range correlations by detrended fluctuation analysis of HRV had advantages over the conventional spectral analysis due to its independence of human behavior [13][14][15] and had been also proved to be useful in diagnosis of cardiovascular diseases [16][17][18].…”

Network analysis of human heartbeat dynamics

“…1,2 The statistical properties of human heartbeat intervals have particularly attracted extensive attentions. [1][2][3][4][5][6][7][8][9][10] The scale-invariant property, long-range correlations, and multifractal features, etc., have been discovered in the human heartbeat rhythm. [4][5][6][7][8][9][10] The purpose of all these studies is double-folded; to uncover the statistical quantities that will identify the mechanisms underlying the complexity in the human heartbeat dynamics, and to provide means of diagnosis and prognosis of heart disease.…”