By Jason H. T. Bates
With mathematical and computational types furthering our realizing of lung mechanics, functionality and sickness, this publication presents an all-inclusive advent to the subject from a quantitative viewpoint. concentrating on inverse modeling, the reader is guided in the course of the thought in a logical development, from the easiest versions as much as cutting-edge types which are either dynamic and nonlinear. Key instruments utilized in biomedical engineering examine, akin to regression idea, linear and nonlinear platforms concept, and the Fourier rework, are defined. Derivations of vital actual ideas, comparable to the Poiseuille equation and the wave pace equation, from first ideas also are supplied. instance purposes to experimental information all through illustrate physiological relevance, when challenge units on the finish of every bankruptcy supply perform and attempt reader comprehension. This publication is perfect for biomedical engineering and biophysics graduate scholars and researchers wishing to appreciate this rising box.
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Additional info for Lung Mechanics: An Inverse Modeling Approach
Sample text
The traditional approach to measuring volume change at the mouth is to use a device known as a spirometer, which is a variablevolume gas reservoir from which a subject breathes. The walls surrounding the reservoir are designed to move as the reservoir changes volume in a way that can be tracked and recorded (the simplest realization of this device is to use a rigid container with its open end inverted over a bath of water, so the height of the container above the surface is proportional to the volume of gas it contains).
A practical issue arises with respect to the number of data points that need to be collected in order to satisfy the sampling theorem. Obviously, this number can become unmanageably large if f0 itself is too large. However, the temptation to under-sample the signal must be resisted at all costs because dropping the sampling frequency below 2f0 does not simply sacrifice the information contained in the higher frequencies. Instead, this information reappears at lower frequencies in the sampled data.
This is the case, for example, when respiratory pressures and flows are recorded for the purpose of calculating the mechanical impedance of the lungs (described in Chapter 8). 5 A continuous sine wave (solid line) oscillating with a frequency f0 is sampled (dots) with a frequency less than 2f0 , yielding a set of discrete points that define an aliased sine wave (dashed line) that has a frequency much less than f0 . be band-limited below some manageable f0 , it is always necessary to pass them through high-quality, low-pass, electronic anti-aliasing filters before they are digitized.