Invention:
The DeScoD-ECG model is a novel electrocardiogram (ECG) baseline wander and noise removal technology that extends the current diffusion model for reconstructing electrocardiogram signals. It establishes a new benchmark in biomedical signal processing to provide a more accurate approximation of ECG signals for detection of cardiovascular irregularities. The DeSCOD-ECG model shows overall improvement on conducted experiments over traditional digital filter-based and deep learning-based methods, leading to better approximations of the true data distribution and higher stability under extreme noise corruption.
Background:
Electrocardiogram signals commonly suffer noise interference such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. This technology extends the conditional diffusion-based generative model for ECG noise removal.
Applications:
- ECG signal processing
- Biomedical engineering
- Cardiovascular disease detection
- Telecommunications (noise reduction and signal enhancement)
Advantages:
- More stable and consistent with different noise levels
- Better approximations of data
- Experimental results demonstrate state-of-the-art performance
