Computer Science > Computer Vision and Pattern Recognition
[Submitted on 2 Jun 2023 (v1), last revised 3 May 2024 (this version, v3)]
Title:DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis
View PDF HTML (experimental)Abstract: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.
Submission history
From: Achraf Ben-Hamadou [view email][v1] Fri, 2 Jun 2023 19:08:31 UTC (34,655 KB)
[v2] Fri, 26 Jan 2024 21:30:17 UTC (12,876 KB)
[v3] Fri, 3 May 2024 08:25:54 UTC (12,937 KB)
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