A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram
| Autor: | Lucas C. Lampier, Yves L. Coelho, Eliete M. O. Caldeira, Teodiano Bastos-Filho |
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| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Ingenius: Revista de Ciencia y Tecnología, Iss 27 (2021) |
| Druh dokumentu: | article |
| ISSN: | 1390-650X 1390-860X |
| DOI: | 10.17163/ings.n27.2022.04 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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