Carl Böck, Kyriaki Kostoglou, Peter Kovacs, Mario Huemer, Jens Meier,
"A Linear Parameter Varying ARX Model for Describing Biomedical Signal Couplings"
: Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS), Serie Lecture Notes in Computer Science (LNCS), Vol. 12014, Springer, Seite(n) 339-346, 4-2020, ISBN: 978-3-030-45096-0
Original Titel:
A Linear Parameter Varying ARX Model for Describing Biomedical Signal Couplings
Sprache des Titels:
Englisch
Original Buchtitel:
Computer Aided Systems Theory - EUROCAST 2019, Part II, Lecture Notes in Computer Science (LNCS)
Original Kurzfassung:
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.
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