Pola S, Macerata A, Emdin M, Marchesi C
Department di Systemi ed Informatica, University di Firenze, Florence, Italy.
IEEE Trans Biomed Eng. 1996 Jan;43(1):46-59. doi: 10.1109/10.477700.
Spectral analysis of cardiovascular series has been proposed as a noninvasive tool for investigating the autonomic control of the cardiovascular system. The analysis of such series during autonomic tests requires high resolution estimators that are capable to track the transients of the tests. A comparative evaluation has been made among classical (FFT based), autoregressive (both block and sequential mode) and time-frequency representation (TFR) based power spectral estimators. The evaluation has been performed on artificial data that have typical patterns of the nonstationary series. The results documented the superiority of the TFR approach when a sharp time resolution is required. Moreover, the test on a RR-like series has shown that the smoothing operation is effective for rejecting TFR cross-terms when a simple, two-three components series is concerned. Finally, the preliminary application of the selected methods to real RR interval time series obtained during some autonomic tests has shown that the TFR are capable to correctly represent the transient of the series in the joint time-frequency domain.
心血管序列的频谱分析已被提议作为一种用于研究心血管系统自主控制的非侵入性工具。在自主测试期间对此类序列进行分析需要高分辨率估计器，这些估计器能够跟踪测试的瞬态变化。已对基于经典（基于快速傅里叶变换）、自回归（块模式和顺序模式）以及基于时频表示（TFR）的功率谱估计器进行了比较评估。该评估是在具有非平稳序列典型模式的人工数据上进行的。结果证明，在需要敏锐的时间分辨率时，TFR方法具有优越性。此外，对类似RR的序列进行的测试表明，对于简单的两三个分量序列，平滑操作对于抑制TFR交叉项是有效的。最后，将所选方法初步应用于在一些自主测试期间获得的实际RR间期时间序列，结果表明TFR能够在联合时频域中正确表示序列的瞬态变化。
