SynthECG: Synthetic 12-Lead ECG Generation

A systematic evaluation framework for generative deep learning on synthetic ECG signals.

Overview

This master’s thesis addressed a core methodological gap in physiological signal generation: the lack of a standardized, reproducible way to evaluate synthetic ECG outputs from generative deep learning models.

The code repository for this project is available on GitHub.

Thesis

  • Title: A Systematic Evaluation Framework of Generative Deep Learning for 10-second 12-lead Synthetic ECG Signals
  • Institution: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS
  • Promotor: Prof. Maarten De Vos
  • Supervisor Lore Van Santvliet
  • Grade: 17/20
  • Full text: KU Leuven repository

Contribution

I developed an end-to-end benchmarking pipeline that enables reproducible training and systematic evaluation of generative models for 12-lead ECG synthesis. The framework supports fair comparison across model families and provides a foundation for more reliable reporting in this area.

Focus Areas

  • Generative deep learning for physiological time series
  • Benchmark design and model comparison
  • Reproducibility in biomedical AI