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First published online August 18, 2005
Journal of Experimental Biology 208, 3221-3232 (2005)
Published by The Company of Biologists 2005
doi: 10.1242/jeb.01762
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Assessing physiological complexity

W. W. Burggren1,* and M. G. Monticino2

1 Department of Biological Sciences
2 Department of Mathematics, University of North Texas, Denton, TX 76203, USA



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  		Fig. 1. Time series trajectories. (A) System behavior converges to a fixed state. Such a pattern is commonly seen in the assessment of phase lag and damping in blood pressure recording systems. (B) Periodic system. Such patterns are evident in stable heart rate recordings. (C) Aperiodic trajectory characteristic of a chaotic system. Patterns like these are characteristic of the abnormal beating of hearts in fibrillation.

 


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  		Fig. 2. Effect of sampling frequency on apparent complexity of heart rate (fH) patterning in the adult moth of the tobacco hornworm, Manduca sexta. (A) Heart rate in the resting, intact adult moth at 20°C. Time marker in 10 s intervals (after Smits et al., 2000Go). (B–E) Effect of sampling frequency on the apparent heart rate pattern observed during a 1.5 min period. Note how in B and C the same low sample frequency can yield a heart rate of zero or alternatively a range from 20 to 70 beats min–1. As sampling frequency increases, the apparent complexity of the observed pattern of heart rate increases. Note that the potential and kinetic complexity remain identical in each case – the change is merely an artifact of sampling frequency.

 


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  		Fig. 3. Changes in predictability and uncertainty as a function of sampling frequency in simple, complicated and complex systems. (A) When sampling times are frequent, the degree of predictability is high (uncertainty low) as time progresses from the last sampling. Note also that the rate of degradation is largest in complex systems and smallest in simple systems. (B). Predictability is degraded (uncertainty increases) rapidly at lower sampling frequencies.

 

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© The Company of Biologists Ltd 2005