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A mathematical model decomposing RR intervals into a deterministic trend and random fluctuation correctly described the data series on a scale of at least 300 beats during stress tests.
A mathematical model decomposing RR intervals into a deterministic trend and random fluctuation accurately describes heart rate dynamics during stress testing.
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May refine HRV assessment during stress testing; leaves open clinical utility pending prospective validation.
The heart beat RR intervals extracted from the electrocardiogram recorded during the stress test show a non stationary profile consisting of a decreasing trend during the exercise phase, an increasing trend during the recovery and a global minimum (acme). In addition this time series exhibits a time-varying variance. We decompose the series into a deterministic trend and random fluctuation. The trend is obtained as an exponential fit of the data; the fluctuation is modeled as a mean reverting process driven by the trend, in which the random innovation has a time-varying variance. Data analysis, performed on ambulatory recorded electrocardiograms of 10 healthy subjects, shows that the model describes correctly the data series on a scale of at least 300 beats.
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Cammarota et al. (2011) studied Healthy (n=10). Stress test was evaluated on Model fit of RR interval series. A mathematical model decomposing RR intervals into a deterministic trend and random fluctuation correctly described the data series on a scale of at least 300 beats during stress tests.
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