Heart rate variability modeling

A three-arousal model for engineering psychophysiology. CNV = contingent negative variation EEG = electroencephalogram EDA = electrodermal activity ERP = event-related potentials HR = heart rate HRV = heart rate variability. 

Wesseling, K. H., Settels, J. J. (1993). Circulatory model of baro-and cardio-pulmonary reflexes. In M. Di Rienzo, G. Mancia, G. Parati, A. Pedotti, A. Zanchetti (Eds.), Blood pressure and heart rate variability. Amsterdam lOS Press. 

In another approach in which heart rate variability, variations in cardiac cycle and arterial blood pressure are modeled, also flow elements are used, in addition, a baroreflex model is introduced, affecting the heart rate and the stroke volume of the heart. By introducing a time delay between the baroreflex input and output, an unstable system is created that continues to oscillate and explains the heart rate variability and variations in the cardiac cycle. Both modeling approaches will be briefly discussed in this chapter. 

Modeling Heart Rate Variability using a Baroreflex Model... 

Fig. 18.14. Heart rate variability versus time using baroreflex model.

We will now discuss how measurements of both Heart Rate Variability and Electromyography were obtained and analysed for use in our model. 

Fig. 4. Change in heart rate produced by apomorphine in the rat. Slowing of heart rate predominates at low dmg concentrations, while tachycardia is most prominent at high steady-state concentration. Two sigmoid Emax models have been combined for the PK-PD analysis. Cp(50) corresponds to Cso% (From Paalzow LK, Paalzow GHM, Tfelt-Hansen P Variability in bioavailability concentration versus effect. In Rowland M, Sheiner LB, Steimer J-L, editors. Variability in dmg therapy description, estimation, and control. New York Raven Press 1985.)...

As with the trends previously mentioned, proposals have been promulgated for internal and external constraints. At first pass, it is tempting to account for relations between life history variables almost purely on the basis of fundamental allometric constraints. Metabolic rate, lifespan, fecundity, age at maturity, and maternal investment all vary with body mass as power functions. In fact, relations are invariant between some of these variables. For example, lifespan scales with body mass by a 1/4 power, and heart rate (or the rate of ATP synthesis) scales with body mass by a — 1/4 power. The product yields an approximately constant number of metabolic events in mammal species, independent of body mass or lifespan. Age at maturity / lifespan, and annual maternal investment / lifespan (for indeterminate growers), are also invariant ratios (Chamov, 1993 Chamov et al., 2001 Steams, 1992). West and Brown (2004) point out that invariant ratios, and universal quarter-power allometric trends in general, suggest underlying physical first principles. They employ their model to explain these life history relations (Enquist et al., 1999 Niklas and Enquist, 2001 West et al., 2001). 

Chapter 14 shows how modeling can propose mechanisms to explain experimentally observed oscillations in the cardiovascular system. A control system characterized by a slow and delayed change in resistance due to smooth muscle activity is presented. Experiments on this model show oscillations in the input impedance frequency spectrum, and flow and pressure transient responses to step inputs consistent with experimental observations. This autoregulation model supports the theory that low-frequency oscillations in heart rate and blood pressure variability spectra (Mayer waves) find their origin in the intrinsic delay of flow regulation. 

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