DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis

• URL: http://arxiv.org/abs/2306.01875v3
• Date: Fri, 3 May 2024 08:25:54 GMT
• Title: DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis
• Authors: Nour Neifar, Achraf Ben-Hamadou, Afef Mdhaffar, Mohamed Jmaiel,
• Abstract summary: We introduce a novel versatile approach based on denoising diffusion probabilistic models for ECG synthesis. Our approach presents the first generalized conditional approach for ECG synthesis. We show that our approach outperforms other state-of-the-art ECG generative models.
• Score: 4.6685771141109305
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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