A combined dynamical sequential network for generating coupled cardiovascular signals with different beat types

We present generalizations of the previously published artificial models for generating abnormal cardiac rhythms to provide simulations of coupled cardiovascular (CV) signals with different beat morphologies. Using a joint dynamical formulation, we generate the normal morphologies of the cardiac cycle using a sum of Gaussian kernels, fitted to real CV recordings. The joint inter-dependencies of CV signals are introduced by assuming the same angular frequency and a phase coupling. Abnormal beats are then specified as new dynamical trajectories. An ergadic first-order Markov chain is also used for switching between normal and abnormal beat types. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the cardiac cycle as a function of the inter-beat interval. We demonstrate an example of the use of this model by simulating abnormal electrocardiographic effects including the ectopy and fusion. In addition, the HR-dependent pulsus phenomena are shown to result for ECG-ABP pairs. The approach presented in this paper may therefore serve as an effective framework for synthetic generation of coupled CV signals with different beat morphologies.