Key result
Deep learning estimates respiratory rate from raw PPG with ~27% lower error than existing methods.
Why the study?
Existing PPG-based respiratory rate estimation methods rely on hand-crafted rules or show sub-optimal performance on real-world data with recent end-to-end deep learning approaches.
Does a CycleGAN-based deep learning approach improve the accuracy of respiratory rate estimation from raw PPG signals compared to existing methods?
Does a CycleGAN-based deep learning approach improve the accuracy of respiratory rate estimation from raw PPG signals compared to existing methods?
Answers are grounded in this paper and cite their sources.
An end-to-end CycleGAN deep learning approach significantly improves the accuracy of respiratory rate estimation from raw PPG signals compared to existing classical and deep learning methods.
Respiratory rate (RR) is a clinical sign representing ventilation. An abnormal change in RR is often the first sign of health deterioration as the body attempts to maintain oxygen delivery to its tissues. There has been a growing interest in remotely monitoring of RR in everyday settings which has made photoplethysmography (PPG) monitoring wearable devices an attractive choice. PPG signals are useful sources for RR extraction due to the presence of respiration-induced modulations in them. The existing PPG-based RR estimation methods mainly rely on hand-crafted rules and manual parameters tuning. An end-to-end deep learning approach was recently proposed, however, despite its automatic nature, the performance of this method is not ideal using the real world data. In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals. Our results demonstrate a higher RR estimation accuracy of up to 2× (mean absolute error of 1.9±0.3 using five fold cross validation) compared to the state-of-th-art using a identical publicly available dataset. Our results suggest that CycleGAN can be a valuable method for RR estimation from raw PPG signals.
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Aqajari et al. (2021) studied Respiratory rate estimation (n=53). Cycle Generative Adversarial Networks (CycleGAN) vs. State-of-the-art RR estimation methods (SmartQualityFusion and Deep Learning method) was evaluated on Mean absolute error (MAE) of respiratory rate estimation (breaths/min). A CycleGAN-based end-to-end deep learning approach estimated respiratory rate from raw PPG signals with a mean absolute error of 1.9 ± 0.3 breaths per minute, outperforming existing methods.
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