Documentation Center

  • Trial Software
  • Product Updates

filtfilt

Zero-phase digital filtering

Syntax

y = filtfilt(b,a,x)
y = filtfilt(SOS,G,x)
y = filtfilt(d,x)

Description

y = filtfilt(b,a,x) performs zero-phase digital filtering by processing the input data, x, in both the forward and reverse directions [1]. filtfilt operates along the first nonsingleton dimension of x. The vector b provides the numerator coefficients of the filter and the vector a provides the denominator coefficients. If you use an all-pole filter, enter 1 for b. If you use an all-zero filter (FIR), enter 1 for a. After filtering the data in the forward direction, filtfilt reverses the filtered sequence and runs it back through the filter. The result has the following characteristics:

  • Zero-phase distortion

  • A filter transfer function, which equals the squared magnitude of the original filter transfer function

  • A filter order that is double the order of the filter specified by b and a

filtfilt minimizes start-up and ending transients by matching initial conditions, and you can use it for both real and complex inputs. Do not use filtfilt with differentiator and Hilbert FIR filters, because the operation of these filters depends heavily on their phase response.

    Note:   The length of the input x must be more than three times the filter order, defined as max(length(b)-1,length(a)-1).

y = filtfilt(SOS,G,x) zero-phase filters the data, x, using the second-order section (biquad) filter represented by the matrix SOS and scale values G. SOS is an L-by-6 matrix containing the L second-order sections. SOS must be of the form

where each row are the coefficients of a biquad filter. The vector of filter scale values, G, must have a length between 1 and L + 1.

    Note:   When implementing zero-phase filtering using a second-order section filter, the length of the input, x, must be more than three times the filter order. You can use filtord to obtain the order of the filter.

y = filtfilt(d,x) zero-phase filters the input data, x, using a digital filter, d. Use designfilt to generate d based on frequency-response specifications.

Examples

expand all

Zero-Phase Filtering of an Electrocardiogram Waveform

Zero-phase filtering helps preserve features in the filtered time waveform exactly where those features occur in the unfiltered waveform.

To illustrate the use of filtfilt for zero-phase filtering, consider an electrocardiogram waveform as an example.

plot(ecg(500))
axis([0 500 -1.25 1.25])
text(155,-.4,'Q'), text(180,1.1,'R'), text(205,-1,'S')

The QRS complex is an important feature in the ECG waveform beginning around time point 160 in this example.

The following example corrupts the ECG waveform with additive noise, constructs a lowpass FIR equiripple filter, and filters the noisy waveform using both zero-phase and conventional filtering. Because the filter is an all-zero (FIR) filter, the denominator equals 1. Seed the random number generator for reproducible results.

rng default

x = ecg(500)'+0.25*randn(500,1);
d = designfilt('lowpassfir','DesignMethod','equiripple', ...
               'PassbandFrequency',0.15,'StopbandFrequency',0.2, ...
               'PassbandRipple',1,'StopbandAttenuation',60);
y = filtfilt(d,x);
y1 = filter(d,x);

subplot(2,1,1)
plot([y y1])
title('Filtered Waveforms')
legend('Zero-phase Filtering','Conventional Filtering')
subplot(2,1,2)
plot(ecg(500))
title('Original Waveform')

Zero-phase filtering reduces noise in the signal and preserves the QRS complex at the same time it occurs in the original signal. Conventional filtering reduces noise in the signal, but delays the QRS complex.

Repeat the above using a Butterworth second-order section filter.

d1 = designfilt('lowpassiir','DesignMethod','butter', ...
                'FilterOrder',12,'HalfPowerFrequency',0.15);
y = filtfilt(d1,x);

subplot(1,1,1)
plot(x,'b-.'), hold on
plot(y,'r','linewidth',3)
legend('Noisy ECG','Zero-phase Filtering','location','NorthEast')

References

[1] Oppenheim, Alan V., Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. 2nd Ed. Upper Saddle River, NJ: Prentice Hall, 1999.

[2] Mitra, Sanjit K. Digital Signal Processing. 2nd Ed. New York: McGraw-Hill, 2001, secs. 4.4.2 and 8.2.5.

[3] Gustafsson, F. "Determining the initial states in forward-backward filtering." IEEE® Transactions on Signal Processing. Vol. 44, April 1996, pp. 988–992.

See Also

| | | |

Was this topic helpful?