Resumen

LAMPIER, Lucas C.; COELHO, Yves L.; CALDEIRA, Eliete M. O.  y  BASTOS-FILHO, Teodiano F.. A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram. Ingenius [online]. 2022, n.27, pp.96-104. ISSN 1390-860X.  https://doi.org/10.17163/ings.n27.2022.09.

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.

Palabras clave : Deep Neural Networks; Photoplethysmography; Respiratory Rate.

        · resumen en Español     · texto en Español     · Español ( pdf )