Bayesian ECG reconstruction using denoising diffusion generative models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913191758397440 |
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| author | Cardoso, Gabriel V. Bedin, Lisa Duchateau, Josselin Dubois, Rémi Moulines, Eric |
| author_facet | Cardoso, Gabriel V. Bedin, Lisa Duchateau, Josselin Dubois, Rémi Moulines, Eric |
| contents | In this work, we propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data that focuses on ECG morphology and inter-lead dependence. Our results show that this innovative generative model can successfully generate realistic ECG signals. Furthermore, we explore the application of recent breakthroughs in solving linear inverse Bayesian problems using DDGM. This approach enables the development of several important clinical tools. These include the calculation of corrected QT intervals (QTc), effective noise suppression of ECG signals, recovery of missing ECG leads, and identification of anomalous readings, enabling significant advances in cardiac health monitoring and diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05388 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian ECG reconstruction using denoising diffusion generative models Cardoso, Gabriel V. Bedin, Lisa Duchateau, Josselin Dubois, Rémi Moulines, Eric Signal Processing Machine Learning In this work, we propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data that focuses on ECG morphology and inter-lead dependence. Our results show that this innovative generative model can successfully generate realistic ECG signals. Furthermore, we explore the application of recent breakthroughs in solving linear inverse Bayesian problems using DDGM. This approach enables the development of several important clinical tools. These include the calculation of corrected QT intervals (QTc), effective noise suppression of ECG signals, recovery of missing ECG leads, and identification of anomalous readings, enabling significant advances in cardiac health monitoring and diagnosis. |
| title | Bayesian ECG reconstruction using denoising diffusion generative models |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2401.05388 |