Key result
RRWaveNet accurately estimates respiratory rate from raw PPG within ~2 breaths per minute.
Why the study?
Standard manual respiratory rate counting is prone to human error and cannot be performed continuously.
Does RRWaveNet improve the accuracy of continuous respiratory rate estimation from raw PPG signals compared to state-of-the-art methods?
Does RRWaveNet improve the accuracy of continuous respiratory rate estimation from raw PPG signals compared to state-of-the-art methods?
RRWaveNet provides an accurate, end-to-end deep learning approach for continuous respiratory rate estimation from raw PPG signals, enabling potential applications in telemedicine and wearable devices.
May enable wearable respiratory monitoring in remote settings; leaves open prospective clinical validation.
Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events, such as heart disease, lung disease, and sleep disorders. Unfortunately, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-to-end deep learning model which does not require feature engineering and can use low-cost raw photoplethysmography (PPG) as input signal. RRWaveNet was tested subject-independently and compared to baseline in four data sets (BIDMC, CapnoBase, WESAD, and SensAI) and using three window sizes (16, 32, and 64 s). RRWaveNet outperformed current state-of-the-art methods with mean absolute errors at an optimal window size of 1.66 ± 1.01, 1.59 ± 1.08, 1.92 ± 0.96, and 1.23 ± 0.61 breaths per minute for each data set. In remote monitoring settings, such as in the WESAD and SensAI data sets, we apply transfer learning to improve the performance using two other ICU data sets as pretraining data sets, reducing the MAE by up to 21%. This shows that this model allows accurate and practical estimation of RR on affordable and wearable devices. Our study also shows feasibility of remote RR monitoring in the context of telemedicine and at home.
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Osathitporn et al. (2023) studied Respiratory rate estimation. RRWaveNet (deep learning model using raw PPG) vs. Current state-of-the-art methods was evaluated on Mean absolute error (MAE) of respiratory rate estimation. RRWaveNet, an end-to-end deep learning model using raw PPG, accurately estimated respiratory rate with mean absolute errors ranging from 1.23 to 1.92 breaths per minute across four datasets.
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