By Claudio Cobelli
This unified modeling textbook for college kids of biomedical engineering presents a whole direction textual content at the foundations, concept and perform of modeling and simulation in body structure and medication. it really is devoted to the wishes of biomedical engineering and medical scholars, supported by way of utilized BME functions and examples. . built for biomedical engineering and comparable classes, with an interdisciplinary clinical/engineering strategy, quantitative foundation, and plenty of utilized examples to augment studying. can provide a quantitative method of modeling and in addition covers simulation. Case stories and engineering functions from BME, plus finish of bankruptcy workouts and Instructor's handbook
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Additional info for Introduction to Modeling in Physiology and Medicine
33 between all measurements separated by this interval. Equally, a correlation also existed between observations that were 54 days apart, and so on. An alternative means of analyzing these rhythms is to describe them as the sum of an arbitrary number of sine waves of different frequencies, phases and amplitudes. A model consisting of a sum of sine waves is known as a spectral representation. From the spectral representation one can construct a spectral density function, which is a graph showing the contributions of each frequency to the observed process.
There are, though, other forms of model. We can have conceptual, mental, verbal, physical, statistical, logical, graphical models, etc. For instance, a standard textbook of physiology may typically include a description of the inhalation of air that results in oxygen entering the bloodstream via the lungs, and the corresponding removal of carbon dioxide that is exhaled back into the atmosphere. This description may well extend to a sizeable word count, and is known as a verbal model of part of the respiratory system.
On the other hand, whilst discrete data points are usually analyzed using discrete time methods, it is possible to convert such discrete data to continuous form by the adoption of appropriate interpolation and extrapolation methods. The emphasis in this chapter is placed upon time domain data. A number of examples of frequency domain approaches are included though, together with means by which time domain data can be converted into frequency domain forms. For more in-depth treatments of frequency domain approaches to data modeling, the reader should consult works such as Marmarelis and Marmarelis (1978) and Westwick and Kearney (2003).