A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

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Lucas C. Lampier https://orcid.org/0000-0002-5141-9480
Yves L. Coelho https://orcid.org/0000-0001-8756-4316
Eliete M. O. Caldeira https://orcid.org/0000-0002-3742-0952
Teodiano F. Bastos-Filho https://orcid.org/0000-0002-1185-2773


This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.
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