Key result
CardioFlow generates high-quality ECGs from PPG data faster than existing diffusion models.
Why the study?
Existing diffusion models generate high-quality ECGs from wearable PPG data but suffer from slow inference due to iterative denoising.
Does CardioFlow generate high-quality ECGs from PPG data faster than existing diffusion-model-based methods?
Does CardioFlow generate high-quality ECGs from PPG data faster than existing diffusion-model-based methods?
CardioFlow offers a computationally efficient method for generating high-quality ECGs from wearable PPG data, overcoming the slow inference times of traditional diffusion models.
This study tackles the problem of generating electrocardiograms (ECG) from photoplethysmograms (PPG) data collected by wearable devices such as smartwatches. Existing methods based on diffusion models successfully generate high-quality ECGs but suffer from slow inference since diffusion models iterate neural network processing to denoise noisy data. In this study, we propose a new solution, called CardioFlow, to this PPG-to-ECG translation problem; based on the recently proposed rectified flow framework, it can be interpreted as a one/few-step generative model. Furthermore, CardioFlow can computationally efficiently generate ECG-specific structures using masks highlighting PPG and ECG signal peaks. We conduct the experiments on two real-world biosignal datasets, WESAD and DALIA, and confirm that our method can generate high-quality ECGs faster than the existing diffusion-model-based methods.
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Nambu et al. (2025) studied Electrocardiogram generation from photoplethysmogram. CardioFlow (rectified flow framework) vs. Existing diffusion-model-based methods was evaluated on Generation of high-quality ECGs from PPG data. CardioFlow, a rectified flow framework, generated high-quality ECGs from PPG data faster than existing diffusion-model-based methods on the WESAD and DALIA datasets.
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