DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis
This addresses data scarcity in ECG analysis for medical applications, though it appears incremental as it builds on existing diffusion models.
The paper tackled the problem of generating synthetic ECG signals for data augmentation in cardiovascular disease detection, introducing a diffusion model that outperforms other state-of-the-art generative models and improves classifier performance.
Within cardiovascular disease detection using deep learning applied to ECG signals, the complexities of handling physiological signals have sparked growing interest in leveraging deep generative models for effective data augmentation. In this paper, we introduce a novel versatile approach based on denoising diffusion probabilistic models for ECG synthesis, addressing three scenarios: (i) heartbeat generation, (ii) partial signal imputation, and (iii) full heartbeat forecasting. Our approach presents the first generalized conditional approach for ECG synthesis, and our experimental results demonstrate its effectiveness for various ECG-related tasks. Moreover, we show that our approach outperforms other state-of-the-art ECG generative models and can enhance the performance of state-of-the-art classifiers.