Comparison of Methods for Generating Synthetic Non-Stationary ECG-Like Signals for Testing Time Series Analysis Algorithms
Abstract
In this paper, various approaches to the generation of synthetic signals simulating a human electrocardiogram (ECG) are considered, with an emphasis on non-stationarity and the presence of various waveforms. Three main types of methods are proposed: 1) rule-based, based on the sum of Gaussians for modeling waves P, Q, R, S, T; 2) stochastic models based on Markov chains, allowing to emulate transitions between different physiological states; 3) neural network generators without strict rules (for example, a recurrent LSTM network with random weights). It is shown how each of the models can be modified to introduce nonstationarity (variations in the duration of cardiac cycles, switching states) and adding local recording artifacts (noisy areas). The proposed methods can be used in testing clustering and time series analysis algorithms when it is necessary to test the methods’ resistance to noise, rare events, and state changes.
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