Evaluating time-varying heart-rate variability power spectral density

Abstract

A multiple weighted-least-square (WLS) identification process is presented for recognizing changes in ICU patient status. An adaptive scheme for the WLS is proposed in which the forgetting factor is automatically driven by the signal characteristics. Generally, adaptive algorithms are more complex and time-consuming than standard WLS, but they show a high tracking performance combined with the benefit of parameter smoothing. Nevertheless, the use of parameter-explicit filtering significantly reduces the computation time. This is a relevant advantage for real-time implementation. This adaptive approach also provides additional information to identify the signal variation speed, which can be used to localize transient phenomena. This article presents the algorithm performance in individuating and tracking the modifications of the cardiac autonomic control. To make data interpretation easier, the time-frequency distributions obtained are displayed as spectrograms. In addition, the signal speed variation is used to draw the attention of the physician to transient episodes.