Bianchi A M, Mainardi L T, Cerutti S
Laboratory of Biomedical Engineering, IRCCS H. S. Raffaele, Milano, Italy.
Methods Inf Med. 1997 Dec;36(4-5):241-5.
Spectral parameters extracted from the heart rate variability signal are obtained on a beat-to-beat basis by means of autoregressive recursive identification. In this paper a whale forgetting window is introduced, instead of the classical exponential one, in order to reduce the noise influence on the estimated parameters. After proper simulation it was found that the whale forgetting window markedly reduces the noise in the identification, but maintains a good response to abrupt changes in the signal. The algorithm was thus applied to the analysis of the HRV data recorded during different transient situations in physiological and pathological conditions. The spectral parameters were obtained on a beat-to-beat basis and their trends were smoother and more accurate with respect to the traditional exponential window also in presence of noise or artifacts in the time series (sudden and short time changes, ectopic beats, etc.), without losing the signal variations of physiological interest.
通过自回归递归识别逐搏获取从心率变异性信号中提取的频谱参数。在本文中，引入了鲸鱼遗忘窗口，而非传统的指数窗口，以减少噪声对估计参数的影响。经过适当的模拟发现，鲸鱼遗忘窗口显著降低了识别中的噪声，但对信号的突然变化仍保持良好响应。该算法因此被应用于生理和病理条件下不同瞬态情况期间记录的心率变异性数据的分析。逐搏获得频谱参数，并且在时间序列中存在噪声或伪迹（突然和短时间变化、异位搏动等）的情况下，相对于传统指数窗口，其趋势更平滑、更准确，同时不会丢失生理相关的信号变化。
