Diffusion-based Conditional ECG Generation with Structured State Space Models

Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series.

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Cite this as

Juan Miguel Lopez Alcaraz, Nils Strodthoff (2024). Dataset: Diffusion-based Conditional ECG Generation with Structured State Space Models. https://doi.org/10.57702/t3degh9l

DOI retrieved: December 2, 2024

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Created December 2, 2024
Last update December 2, 2024
Author Juan Miguel Lopez Alcaraz
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Nils Strodthoff
Homepage https://zenodo.org/account/settings/github/repository/AI4HealthUOL/SSSD-ECG