A Linear Parameter Varying ARX Model for Describing Biomedical Signal Couplings

Biomedical signal processing frequently deals with information extraction for clinical decision support. A major challenge in this field is to reveal diagnostic information by eliminating undesired interfering influences. In case of the electrocardiogram, e.g., a frequently arising interference is caused by respiration, which possibly superimposes diagnostic information. Respiratory sinus arrhythmia, i.e., the acceleration and deceleration of the heartrate (HR) during inhalation and exhalation, respectively, is a well-known phenomenon, which strongly influences the ECG. This influence becomes even more important, when investigating the so-called heart rate variability, a diagnostically powerful signal derived from the ECG. In this work, we propose a model for capturing the relationship between the HR and the respiration, thereby taking the time-variance of physiological systems into account. To this end, we show that so-called linear parameter varying autoregressive models with exogenous input are well suited for modeling the coupling between the two signals of interest.