ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model

TL;DR

ECGTwin is a novel two-stage framework that generates personalized ECG signals with high fidelity and controllability, using contrastive learning for feature extraction and a diffusion model for synthesis, aiming to improve personalized healthcare.

Contribution

The paper introduces ECGTwin, a new method combining contrastive learning and diffusion models for personalized ECG generation with controllable conditions.

Findings

High-quality ECG generation with diversity and individual features

Enhanced ECG auto-diagnosis performance

Effective integration of personal features and conditions

Abstract

Personalized electrocardiogram (ECG) generation is to simulate a patient's ECG digital twins tailored to specific conditions. It has the potential to transform traditional healthcare into a more accurate individualized paradigm, while preserving the key benefits of conventional population-level ECG synthesis. However, this promising task presents two fundamental challenges: extracting individual features without ground truth and injecting various types of conditions without confusing generative model. In this paper, we present ECGTwin, a two-stage framework designed to address these challenges. In the first stage, an Individual Base Extractor trained via contrastive learning robustly captures personal features from a reference ECG. In the second stage, the extracted individual features, along with a target cardiac condition, are integrated into the diffusion-based generation process through our novel AdaX Condition Injector, which injects these signals via two dedicated and specialized pathways. Both qualitative and quantitative experiments have demonstrated that our model can not only generate ECG signals of high fidelity and diversity by offering a fine-grained generation controllability, but also preserving individual-specific features. Furthermore, ECGTwin shows the potential to enhance ECG auto-diagnosis in downstream application, confirming the possibility of precise personalized healthcare solutions.