A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

Wee Jian Chin , Ban-Hoe Kwan, Wei Yin Lim , Yee Kai Tee, Shalini Darmaraju, Haipeng Liu, Choon-Hian Goh

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Abstract

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.
Original languageEnglish
Article number284
Number of pages17
JournalDiagnostics
Volume14
Issue number3
Early online date28 Jan 2024
DOIs
Publication statusE-pub ahead of print - 28 Jan 2024

Bibliographical note

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Funder

This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (No. FRGS/1/2023/SKK05/UTAR/02/1 to CHG).

Keywords

  • neural network
  • photoplethysmogram
  • deep learning
  • respiratory rate

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