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. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.


page 1

page 7

page 8


Synthetic ECG Signal Generation using Probabilistic Diffusion Models

Deep learning image processing models have had remarkable success in rec...

Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models

The field of deep-learning-based ECG analysis has been largely dominated...

Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata

Deep learning has emerged as the preferred modeling approach for automat...

DiffECG: A Generalized Probabilistic Diffusion Model for ECG Signals Synthesis

In recent years, deep generative models have gained attention as a promi...

Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports

Electrocardiogram (ECG) synthesis is the area of research focused on gen...

Conditional Simulation Using Diffusion Schrödinger Bridges

Denoising diffusion models have recently emerged as a powerful class of ...

Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation

Obtaining per-beat information is a key task in the analysis of cardiac ...

Please sign up or login with your details

Forgot password? Click here to reset