DiffECG: A Generalized Probabilistic Diffusion Model for ECG Signals Synthesis

Autor:	Neifar, Nour, Ben-Hamadou, Achraf, Mdhaffar, Afef, Jmaiel, Mohamed
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (cs.LG)
Popis:	In recent years, deep generative models have gained attention as a promising data augmentation solution for heart disease detection using deep learning approaches applied to ECG signals. In this paper, we introduce a novel approach based on denoising diffusion probabilistic models for ECG synthesis that covers three scenarios: heartbeat generation, partial signal completion, and full heartbeat forecasting. Our approach represents 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. under review
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cb96d1cc1316af612dbab18b5a3df9d http://arxiv.org/abs/2306.01875 Zobrazit plný text záznamu