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BSPII-ch3-filtering-time-2008

Blent ylmaz b bme402 biomedical signal processing ii

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Unformatted text preview: ng- 10 5 Example: Noisy ECG signal • Two ECG cycles extracted using trigger points from crosscorrelation function Synchronized averaging performed – 11 cycles averaged • Dr. Bülent Yılmaz Bü BME402: Biomedical Signal Processing-II Processing- 11 Problem: • Propose a time-domain technique to remove random noise given “only one realization” of the signal or event of interest. Dr. Bülent Yılmaz Bü BME402: Biomedical Signal Processing-II Processing- 12 6 Solution: Moving-average filters • Temporal averaging for noise removal • y[n] = Σ bk x[n-k] – bk: filter coefficients, k = 0 … N – N: order of the filter – The effect of division by the number of samples used is included in the values of filter coefficients Dr. Bülent Yılmaz Bü BME402: Biomedical Signal Processing-II Processing- 13 MAF • H(z)=b0+b1z-1+b2z-2+…+bNz-N Special MAF: Hanning filter • H(z)=(1/4)[1+2z-1+z-2] – Double zero at z=-1 Dr. Bülent Yılmaz Bü BME402: Biomedical Signal Processing-II Processing14 7 Advantages and attributes of MAF • h[k] has a finite number of terms • FIR filter • The output depends only on the present input • • – No recursion, no feedback recur...
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