Generating 24-hour ECG, BP and respiratory signals with realistic linear and nonlinear clinical characteristics using a nonlinear model

A nonlinear model for generating lifelike humaia 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 wing a modified version of entry no.201 of the CinC 2002 24-hour RR interval generator challenge. (such that the user can spec& 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 dcyering lengths, mean heart rates (HR),L F/HF ratios and standard deviations. The length of time in each state is governed by a power law distribution with marhd di$- ferences between waking and sleep states. The statistics of each RR time series segment (a state) can be fully spec$ed by its mean (HR) and spectral distribucion (LF/HF rario). The resultant ECG is shown to exhibit realistic QRSand Qrdispersions, 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 porameters 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 generatol; realistic short and long range blood pressure fluctuations are shown to result. Together with seeded RR interval dynamics, the morphology of the signals corn be fully specified by three parameters per feature and therefore a large range of drzerent (deteiministic) signals can be generated with fully known characteristics, to facil- tate the tesfing of signal processing algorithms. Open source C, Matlab and Java programs for generating the model are available from Physionet.