Biomedical Signal Processing by Metin Akay (Auth.)

By Metin Akay (Auth.)

Refined thoughts for sign processing are actually to be had to the biomedical professional! Written in an easy-to-read, common variety, Biomedical sign Processing offers thoughts to cast off historical past noise, increase sign detection, and research desktop info, making effects effortless to realize and follow. as well as analyzing concepts for electric sign research, filtering, and transforms, the writer provides an in depth appendix with a number of computing device courses that exhibit concepts awarded within the textual content

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E X A M P L E . 4 s h o w s the block diagram of the cascaded canonical realization. 6 Direct-Form Realization of FIR Systems T h e F I R s y s t e m s have the same realization form as the IIR s y s t e m s . The F I R system r e s p o n s e , y(n), can be e x p r e s s e d as a function of input 38 3. Digital Filter D e s i g n y 1(n)=x 2(n) y 2(n) Xi(n) Fig. 4 s a m p l e s , x(n), and the impulse r e s p o n s e , h(q), as follows [8,9,10]: y(n) = Σ h(q)x(n - q). 7) q=0 Please note the similarities b e t w e e n E q s .

Digital Filter D e s i g n T h e analog equivalent highpass filters, Ω ρ and Ω 5, can be calculated by using the digital highpass filter p a r a m e t e r s , ω ρ and ω 5, as follows: « p - cot {f ( f ) . cot n . - « ( f ) - « ( $ . 68) T h e filter o r d e r , N, and cutoff frequency, f l c , can be calculated using E q s . 53). 8 55 D e s i g n o f IIR Digital Filters 1 Η (f) I t Fig. /sr fc = — arccot(n c). 78) 77 T h e important distinction that must be m a d e b e t w e e n lowpass and highpass filters lies in the fact that the lowpass-to-highpass bilinear trans­ formation requires the substitution ζ —> ~z.

4 Canonical Form The canonical form as shown in Fig. 3 for Μ = Ν can also be ex­ pressed by implementing system difference equations [2,6,11]. T h e internal states, can be estimated from the input sample, x(n), and the previous values of the internal states, Vi(n), as follows: v(n) = x(n) - a\v(n - 1) - a2v(n - 2) - · · · - aN(n - N). T h e output, y(n), can be obtained using the summation of the previous and past values of the internal states, y(n) = b0v(n) + b\v(n - 1) + · · • + bNv(n - N). T h e internal states are written as Vj(n) = vn-i, / = 0, 1, 2, .

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