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G. Clifford and P.E. McSharry (2004) Generating 24-Hour ECG, BP and Respiratory Signals with Realistic Linear and Nonlinear Clinical Characteristics Using a Nonlinear Model Computers in Cardiology

Abstract

A nonlinear model for generating lifelike human ECG, blood pressure and respiratory signals is described. Each cycle of the model corresponds to one heart beat and the signals therefore exhibit beat-to-beat fluctuations by driving the model with a sequence of RR intervals. By using a modified version of entry no.201 of the CinC 2002 24-hour RR interval generator challenge, (such that the user can specify the probability of ectopy or artefact) and coupling it to three ordinary differential equations, the model generates a 24-hour ECG signal. Using both standard linear metrics, and nonlinear long range statistics, the signal is shown to exhibit many of the known characteristics such as Respiratory Sinus Arrhythmia, Mayer waves and an overall diurnal rhythm. The RR interval time series is modelled as a set of stationary states (joined by a transient heart rate overshoot) of differing lengths, mean heart rates (HR), LF/HF ratios and standard deviations. The length of time in each state is governed by a power law distribution with marked differences between waking and sleep states. The statistics of each RR time series segment (a state) can be fully specified by its mean (HR) and spectral distribution (LF/HF ratio). The resultant ECG is shown to exhibit realistic QRS- and QT-dispersions, R-S amplitude modulation and Respiratory Sinus Arrhythmia in the short term and normal values for nonlinear statistics (such as entropy) in the long term. By altering the parameters of the ECG model, introducing a heart-rate dependent delay (to simulate pulse transit time), and coupling the baseline to the long-term fluctuations of the 24 hour RR interval generator, realistic short and long range blood pressure fluctuations are shown to result. Together with seeded RR interval dynamics, the morphology of the signals can be fully specified by three parameters per feature and therefore a large range of different (deterministic) signals can be generated with fully known characteristics, to facilitate the testing of signal processing algorithms. Open source C, Matlab and Java programs for generating the model are available from Physionet.