Time Series Analysis of Cardiovascular Biodata

Analyzing and Classifying Cardiac Biosignals

The electrocardiogram (ECG) provides a noninvasive transthoracic interpretation of the electrical activity of the heart. Cardiovascular dysfunction often manifests itself in characteristic alterations of the ECG, in the heart rate variability and in the corresponding patterns of so-called beat-to-beat intervals (BBI), i.e. periods of time between consecutive heart beats (or QRS-complexes; see Fig. 1A). The ability to classify physiological and pathological BBI patterns is critically important for the development of new diagnostic tools.

Characterization of multiple spiral waves

This project aims at characterizing a multiple spiral wave system from a large-scale perspective. Our goal was to develop improved intuition into the complex behavior of these systems for possible applications to the study and diagnosis of cardiac tissue during fibrillation [1]. Accordingly, we focused our study on two quantities that are defined from the state of the entire system. The quantities correspond to the classical predator and prey quantities.

Synchronization Patterns in Transient Spiral Wave Dynamics

An important approach for analyzing spatially extended systems is “network analysis” (also called graphical methods) where time series are observed (or measured) at different spatial locations and then investigated with respect to potential interrelations between different parts or regions of the system. If strong relations are found this is often interpreted as being the result of structural inhomogeneities, hidden connections or other causalities. In view of applications to experimental heart data we have applied this approach to spatiotemporal dynamics of a homogeneous excitable medium exhibiting periodic dynamics in terms of (multiple) spiral waves (Fig.

Time Series Analysis of Cardiovascular Biodata

Characterization and classification of cardiac dynamics on the basis of measured time series (e.g. electrocardiogram, ECG) is crucial for distinguishing physiological from pathological states with potential applications for diagnosis and risk assessment. Since the heart is an extended system, its spatiotemporal electro-mechanical activities in combination with additional control loops of the cardiovascular system form the basis of most observed signals and any (nonlinear) response to perturbations. In particular, complex spatiotemporal dynamics in terms of rotating action potential waves called spiral waves and their subsequent breakup into additional spiral waves has a strong impact on heart rate variability and may cause many cardiac disorders.