Respiratory impedance spectral estimation for digitally created random noise

Measurement of respiratory input mechanical impedance (Z_rs) is noninvasive, requires minimal subject cooperation, and contains information related to mechanical lung function. A common approach to measure Z_rs is to apply random noise pressure signals at the airway opening, measure the resulting flow variations, and then estimate Z_rs using Fast-Fourier Transform (FFT) techniques. The goal of this study was to quantify how several signal processing issues affect the quality of a Z_rs spectral estimate when the input pressure sequence is created digitally. Random noise driven pressure and flow time domain data were simulated for three models, which permitted predictions of Z_rs characteristics previously reported from 0–4, 4–32, and 4–200 Hz. Then, the quality of the Z_rs estimate was evaluated as a function of the number of runs ensemble averaged, windowing, flow signal-to-noise ratio (SNR), and pressure spectral magnitude shape |P(jω)|. For a |P(jω)| with uniform power distribution and a SNR<100, the 0–4 Hz and 4–200 Hz Z_rs estimates for 10 runs were poor (minimum coherence γ²<0.75) particularly where Z_rs is high. When the SNR>200 and 10 runs were averaged, the minimum γ² >0.95. However, when |P(jω)| was matched to |Z_rs|, γ² > 0.91 even for 5 runs and a SNR of 20. For data created digitally with equally spaced spectral content, the rectangular window was superior to the Hanning. Finally, coherence alone may not be a reliable measure of Z_rs quality because coherence is only an estimate itself. We conclude that an accurate estimate of Z_rs is best obtained by matching |P(jω)| to |Z_in| (subject and speaker) and using rectangular windowing.