The purpose of this study is to create a phenomenological model of the human electrocardiogram based on the McSharry model and to achieve a plausible distribution of interpeak intervals between individual heartbeats.

Methods. This paper presents an advanced approach to synthetic ECG generation using a modified McSharry model. We used chaotic dynamics instead of conventional pseudorandom number generators to better represent the variability in ECG dynamical parameters, such as interpeak intervals. A fourth-order circuit equation with a memristor is introduced as a chaos generator. By adjusting the parameters of this system, one can varythe range of peak parameters in the synthetic ECG. The proposed ECG generator can be implemented as a  computer model or as an analog circuit, depending on the application requirements.

Results. The experimental investigation of generated synthetic signals with time-domain waveforms, phase portraits, and RR tachograms’ analysis demonstrated a good correspondence between the synthetic and real ECGs. It is shown that the modified ECG generation approach provides a reasonably realistic and robust method for simulating synthetic ECG signals.

Conclusion. The reported solution possesses many possible applications such as the calibration of medical cardiographs, medical education, and machine learning models for ECG analysis.