Open AccessA Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram
Author(s) -
Lucas Lampier,
Yves Coelho,
Eliete Caldeira,
Teodiano Bastos
Publication year - 2021
Publication title -
ingenius
Language(s) - English
Resource type - Journals
eISSN - 1390-860X
pISSN - 1390-650X
DOI - 10.17163/ings.n27.2022.09
Subject(s) - photoplethysmogram , computer science , respiratory rate , word error rate , artificial neural network , artificial intelligence , deep learning , deep neural networks , pattern recognition (psychology) , speech recognition , heart rate , medicine , blood pressure , computer vision , filter (signal processing)
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.