# Flexible framework for generating synthetic electrocardiograms and photoplethysmograms

arXiv: https://arxiv.org/abs/2408.16291 · HTML: https://mlsanity.com/paper/2408.16291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [Synthetic ECG Signal Generation Using Generative Neural Networks](https://mlsanity.com/paper/2112.03268/) (2021)

## Abstract (arXiv)

By generating synthetic biosignals, the quantity and variety of health data can be increased. This is especially useful when training machine learning models by enabling data augmentation and introduction of more physiologically plausible variation to the data. For these purposes, we have developed a synthetic biosignal model for two signal modalities, electrocardiography (ECG) and photoplethysmography (PPG). The model produces realistic signals that account for physiological effects such as breathing modulation and changes in heart rate due to physical stress. Arrhythmic signals can be generated with beat intervals extracted from real measurements. The model also includes a flexible approach to adding different kinds of noise and signal artifacts. The noise is generated from power spectral densities extracted from both measured noisy signals and modeled power spectra. Importantly, the model also automatically produces labels for noise, segmentation (e.g. P and T waves, QRS complex, for electrocardiograms), and artifacts. We assessed how this comprehensive model can be used in practice to improve the performance of models trained on ECG or PPG data. For example, we trained an LSTM to detect ECG R-peaks using both real ECG signals from the MIT-BIH arrythmia set and our new generator. The F1 score of the model was 0.83 using real data, in comparison to 0.98 using our generator. In addition, the model can be used for example in signal segmentation, quality detection and bench-marking detection algorithms. The model code has been released in urlhttps://github.com/UTU-Health-Research/frameworkforsyntheticbiosignals