A dynamical model for generating synthetic electrocardiogram signals

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Optimal selection of wavelet basis function applied to ECG signal denoising (2006, background): “Though literature related to other time-frequency methods applied to the ECG signal processing is available, however, wavelet based technique is preferred as it has demonstrated better detection accuracy [3].”
Motion artifact reduction in photoplethysmography using independent component analysis (2006, methodology): “associated with heart pulsation is inherently quasi-periodic [5].”
Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs (2011, methodology): “To illustrate the relevance of the proposed model, artificial electrocardiogram (ECG) signals generated using ECGSYN [4] and real ECG recordings from the Physiobank database [5] are used in the experiments.”
Synthesis of Realistic ECG using Generative Adversarial Networks (2019, background): “Previous approaches include the creation of dynamical models to produce synthetic ECG signals [4].”
A Nonlinear Bayesian Filtering Framework for ECG Denoising (2007, methodology): “The framework presented in this paper is based on a dynamic model, previously developed for the generation of synthetic PQRST complexes with their relationship to the beat-tobeat RR-interval timing [17], [18].”