Heart Rate Generation Algorithm Using Generative Adversarial Networks Based on Time-Frequency Domain Composite Loss Values

With the proliferation of smart medical devices, there is a significant need for extensive data collection, which raises concerns regarding privacy protection and data collection costs. In recent years, the development of Generative Adversarial Network (GAN) technology for heart rate signal generation has demonstrated superior generative capabilities and data diversity. In response, we designed a frequency domain loss function tailored to the frequency component of time-series signals, combined with a frequency domain attention mechanism, and integrated the advantages of Transformers to further optimize the model fitting. We assessed the model's generative performance using RMSE and MAE metrics. The results indicate that our model, which utilizes a hybrid time-frequency domain training approach, performs better than similar models, thus proving its effectiveness.