Davis K A, Lutchen K R
Department of Biomedical Engineering, Boston University, MA 02215.
Int J Biomed Comput. 1991 Mar-Apr;27(3-4):261-76. doi: 10.1016/0020-7101(91)90067-o.
Most current techniques to estimate respiratory system mechanical input impedance spectra (Zrs) use digitally created (Fourier transform (FFT) based) random noise. Recent Zrs data reported from 0.1-4 Hz and above 32 Hz display sharper, more distinct spectral features. When expressing Zrs as a power spectral density (PSD), such features suggest the application of a time series spectral estimation approach. Here, a simulation study was performed to compare the quality of impedance (PSD and Zrs) estimates from the time series technique to those from the FFT approach in the presence of measurement noise. Random noise pressure and flow time domain sequences were simulated for two different networks, one which exhibits impedance features reported from 0.1-4 Hz and one which exhibits impedance features reported above 32 Hz. In the time series method, autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models were fit to the pressure and flow sequences separately. The estimated PSD and complex Zrs spectra were compared to the true spectra calculated from the models. Results show that the time series PSD estimates were reasonable even in the presence of additive measurement noise. Conversely, with additive noise, the time series estimates of the complex Zrs showed a negative real part which is physiologically inappropriate. This occurs because of the loss of phase information inherent to the time series approach. Regardless of measurement noise, the FFT estimates of impedance were always close to the true impedance and always superior to the time series estimates. We conclude that an accurate estimate of the PSD or complex Zrs spectra from digitally created FFT-based random noise is best obtained using the traditional FFT method.
目前大多数用于估计呼吸系统机械输入阻抗谱（Zrs）的技术都使用数字生成的（基于傅里叶变换（FFT））随机噪声。最近报道的0.1 - 4 Hz及32 Hz以上的Zrs数据显示出更清晰、更明显的频谱特征。当将Zrs表示为功率谱密度（PSD）时，这些特征表明可应用时间序列谱估计方法。在此，进行了一项模拟研究，以比较在存在测量噪声的情况下，时间序列技术与FFT方法对阻抗（PSD和Zrs）估计的质量。针对两个不同的网络模拟了随机噪声压力和流量时域序列，一个网络呈现0.1 - 4 Hz报道的阻抗特征，另一个呈现32 Hz以上报道的阻抗特征。在时间序列方法中，分别将自回归（AR）、移动平均（MA）和自回归移动平均（ARMA）模型拟合到压力和流量序列。将估计的PSD和复Zrs谱与从模型计算出的真实谱进行比较。结果表明，即使存在加性测量噪声，时间序列PSD估计也是合理的。相反，对于加性噪声，复Zrs的时间序列估计显示出负实部，这在生理上是不合适的。这是由于时间序列方法固有的相位信息丢失所致。无论测量噪声如何，阻抗的FFT估计始终接近真实阻抗，并且始终优于时间序列估计。我们得出结论，使用传统的FFT方法能从基于FFT数字生成的随机噪声中最好地获得PSD或复Zrs谱的准确估计。
