Key result
Genetic programming model generates synthetic PPG signals with ~0.999 correlation, outperforming other methods.
Why the study?
Developing generative models for synthetic PPG signals requires overcoming challenges of limited data availability and the need for data diversity to train deep learning models.
Does a genetic programming approach improve the generation of synthetic PPG signals compared to conventional approaches?
Does a genetic programming approach improve the generation of synthetic PPG signals compared to conventional approaches?
A genetic programming-based generative model can effectively and accurately generate synthetic PPG signals, providing a solution for limited training data in resource-constrained environments.
May aid synthetic PPG data augmentation in resource-limited settings; leaves open clinical validation and workflow integration.
Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample. Unlike conventional regression, the GP approach automatically determines the structure and combinations of a mathematical model. Given that mean square error (MSE) of 0.0001, root mean square error (RMSE) of 0.01, and correlation coefficient of 0.999, the proposed approach outperformed other approaches and proved effective in terms of efficiency and applicability in resource-constrained environments.
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Ghasemi et al. (2024) studied Cardiac monitoring (PPG signal generation). Genetic programming (GP) approach for synthetic PPG signal generation vs. Other approaches (conventional regression) was evaluated on Mean square error (MSE), root mean square error (RMSE), and correlation coefficient. A genetic programming-based generative model successfully generated synthetic PPG signals with high accuracy (MSE 0.0001, RMSE 0.01, correlation coefficient 0.999), outperforming other approaches.
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