The mechanical impedance of the respiratory system Zrs is usually measured by forced excitation while the patient breathes spontaneously. Pressure and flow signals due to breathing contaminate the excitation signals, leading to a poor signal-to-noise ratio (SNR) and thus to errors in impedance estimation, especially at low frequencies (up to 8 Hz). To enhance SNR in the recorded signals we designed an infinite impulse response digital filter for the frequent case in which the excitation is pseudorandom. The algorithm is based on narrowband second-order bandpass elements centred at the excitation frequencies. The performance of the filter was assessed in a simulation study by superposing forced excitation signals (2-32 Hz) from a reference model and the signals of breathing recorded from 16 subjects. When compared with a conventional high-pass filtering, the devised filtering resulted in an increase in SNR which was almost constant over the whole frequency band: 6.30 +/- 0.98 dB (mean +/- SD). This improvement in SNR was reflected in an increase in the number of subjects for which the corresponding coherence y2 attained a value greater than the conventional threshold of acceptability (y2 = 0.95). At the lowest frequency (2 Hz) only two (12.5 per cent) simulated subjects had y2 greater than or equal to 0.95 with the conventional high-pass filtering. By contrast, when using the devised comb filter the number of subjects with y2 greater than or equal to 0.95 increased up to 13 (81 per cent). The results obtained suggest that this filter may be useful to improve SNR and thus Zrs estimation.