Respiratory rate (RR) is an important vital sign marker of health, and it is often neglected due to a lack of unobtrusive sensors for objective and convenient measurement. The respiratory modulations present in simple photoplethysmogram (PPG) have been useful to derive RR using signal processing, waveform fiducial markers, and hand-crafted rules. An endto-end deep learning approach based on residual network (ResNet) architecture is proposed to estimate RR using PPG. This approach takes time-series PPG data as input, learns the rules through the training process that involved an additional synthetic PPG dataset generated to overcome the insufficient data problem of deep learning, and provides RR estimation as outputs. The inclusion of a synthetic dataset for training improved the performance of the deep learning model by 34%. The final mean absolute error performance of the deep learning approach for RR estimation was 2.5±0.6 brpm using 5-fold cross-validation in two widely used public PPG datasets (n=95) with reliable RR references. The deep learning model achieved comparable performance to that of a classical method, which was also implemented for comparison. With large real-world data and reference ground truth, deep learning can be valuable for RR or other vital sign monitoring using PPG and other physiological signals.

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