By Katarzyna J. Blinowska, Jaroslaw Zygierewicz
Sensible Biomedical sign research utilizing MATLAB® offers a coherent remedy of assorted sign processing tools and purposes. The publication not just covers the present thoughts of biomedical sign processing, however it additionally deals assistance on which equipment are applicable for a given job and forms of facts. the 1st a number of chapters of the textual content describe sign research techniques—including the latest and such a lot complex methods—in a simple and available approach. MATLAB exercises are indexed whilst on hand and freely to be had software program is mentioned the place acceptable. the ultimate bankruptcy explores the applying of the tips on how to a wide variety of biomedical indications, highlighting difficulties encountered in perform. A unified review of the sphere, this publication explains the right way to appropriately use sign processing concepts for biomedical functions and steer clear of misinterpretations and pitfalls. It is helping readers to decide on the proper procedure in addition to layout their very own equipment.
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Additional resources for Practical Biomedical Signal Analysis Using MATLAB® (Series in Medical Physics and Biomedical Engineering)
Example text
The surrogate data can be generated by shuffling the time-order of the original time series. The surrogate data will have the same amplitude distribution as the original data, but any temporal correlations that may have been in the original data are destroyed. The test for linearity corresponds to the null hypothesis that all the structure in the time series is given by the autocorrelation function, or equivalently, by the Fourier power spectrum. The test may be performed by fitting autoregressive model to the series and examination of the residuals, or by randomizing the phases of the Fourier transform.
FIR filters can be designed by means of the following functions: fir1 - allows for design of classical lowpass, bandpass, highpass, bandstop filters. It implements an algorithm based on Fourier transform. If the ideal transfer function is Hid ( fn ), its inverse Fourier transform is hid (n) and w(n) is a window (by default a Hamming window) then the filter coefficients are b(n) = w(n)hid (n) for n ∈ [1, N], N is the filter order. This filter has group delay τg = N/2. © 2012 by Taylor & Francis Group, LLC Practical Biomedical Signal Analysis Using MATLAB R 26 fir2 - this function allows to specify arbitrary piecewise linear magnitude response.
The © 2012 by Taylor & Francis Group, LLC Single channel signal a1,1 a1,2 S1 k 31 C S 2 a2,2 a2,1 b2,1 b1,2 b1,1 b2,2 " Y2 Ø Y1 (a) S(t) : S1 G S2 G S2 G S1 G S2 Y (t) : Y2 Y1 Y2 Y1 Y1 . . . 2: (a) An example of two state Hidden Markov model. S1 and S2 are states of the model, ai, j are probabilities of transition from state i to j. Y1 and Y2 are observations, bi, j are probabilities of observation Y j if the system is in state Si . (b) A possible sequence of states and observations.