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Biosignal and Medical Image Processing: MATLAB-Based by John L. Semmlow

Posted On March 24, 2017 at 1:13 am by / Comments Off on Biosignal and Medical Image Processing: MATLAB-Based by John L. Semmlow

By John L. Semmlow

Depending seriously on MATLAB® difficulties and examples, in addition to simulated info, this text/reference surveys an enormous array of sign and snapshot processing instruments for biomedical functions, offering a operating wisdom of the applied sciences addressed whereas showcasing invaluable implementation strategies, universal pitfalls, and crucial program strategies. the 1st and merely textbook to provide a hands-on instructional in biomedical sign and picture processing, it bargains a different and confirmed method of sign processing guide, in contrast to the other competing resource at the subject. The textual content is observed via a CD with aid info records and software program together with all MATLAB examples and figures present in the textual content.

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Additional info for Biosignal and Medical Image Processing: MATLAB-Based Applications (Signal Processing and Communications)

Example text

The arithmetic quantities of mean and variance are frequently used in signal processing algorithms, and their computation is well-suited to discrete data. The mean value of a discrete array of N samples is evaluated as: x¯ = 1 N N ∑x (2) k k=1 Note that the summation in Eq. (2) is made between 1 and N as opposed to 0 and N − 1. This protocol will commonly be used throughout the text to be compatible with MATLAB notation where the first element in an array has an index of 1, not 0. Frequently, the mean will be subtracted from the data sample to provide data with zero mean value.

Note that this highest frequency component may come from a noise source and could be well above the frequencies of interest. The inverse of this rule is that any signal that contains frequency components greater than twice the sampling frequency cannot be reconstructed, and, hence, its digital representation is in error. Since this error is introduced by undersampling, it is inherent in the digital representation and no amount of digital signal processing can correct this error. The specific nature of this under-sampling error is termed aliasing and is described in a discussion of the consequences of sampling in Chapter 2.

If x is a matrix then the output is a row vector resulting from applying the appropriate calculation (mean, variance, or standard deviation) to each column of the matrix. 2. The program first loads the eye movement data (load verg1), then plots the ensemble. The ensemble average is determined using the MATLAB mean routine. Note that the data matrix, data_out, must be in the correct orientation (the responses must be in rows) for routine mean. If that were not the case (as in Problem 1 at the end of this chapter), the matrix transposition operation should be performed*.

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