f-GAN: A frequency-domain-constrained generative adversarial network for PPG to ECG synthesis

• URL: http://arxiv.org/abs/2406.16896v1
• Date: Wed, 15 May 2024 18:53:05 GMT
• Title: f-GAN: A frequency-domain-constrained generative adversarial network for PPG to ECG synthesis
• Authors: Nathan C. L. Kong, Dae Lee, Huyen Do, Dae Hoon Park, Cong Xu, Hongda Mao, Jonathan Chung,
• Abstract summary: Electrocardiograms (ECGs) and photoplethysmograms ( PPGs) are generally used to monitor an individual's cardiovascular health. In clinical settings, ECGs and PPGs are the main signals used for assessing cardiovascular health, but the equipment necessary for their collection precludes their use in daily monitoring. We would like to combine the ease with which PPGs can be collected with the information that ECGs provide about cardiovascular health by developing models to synthesize ECG signals from paired PPG signals.
• Score: 5.206775979957893
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Electrocardiograms (ECGs) and photoplethysmograms (PPGs) are generally used to monitor an individual's cardiovascular health. In clinical settings, ECGs and fingertip PPGs are the main signals used for assessing cardiovascular health, but the equipment necessary for their collection precludes their use in daily monitoring. Although PPGs obtained from wrist-worn devices are susceptible to noise due to motion, they have been widely used to continuously monitor cardiovascular health because of their convenience. Therefore, we would like to combine the ease with which PPGs can be collected with the information that ECGs provide about cardiovascular health by developing models to synthesize ECG signals from paired PPG signals. We tackled this problem using generative adversarial networks (GANs) and found that models trained using the original GAN formulations can be successfully used to synthesize ECG signals from which heart rate can be extracted using standard signal processing pipelines. Incorporating a frequency-domain constraint to model training improved the stability of model performance and also the performance on heart rate estimation.

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