Cargando…

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model

The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous alg...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Qi, Liu, Fang, Song, Yide, Fan, Xiaoya, Wang, Yu, Yao, Yudong, Mao, Qian, Zhao, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525435/
https://www.ncbi.nlm.nih.gov/pubmed/37760126
http://dx.doi.org/10.3390/bioengineering10091024
_version_ 1785110783529582592
author Zhao, Qi
Liu, Fang
Song, Yide
Fan, Xiaoya
Wang, Yu
Yao, Yudong
Mao, Qian
Zhao, Zheng
author_facet Zhao, Qi
Liu, Fang
Song, Yide
Fan, Xiaoya
Wang, Yu
Yao, Yudong
Mao, Qian
Zhao, Zheng
author_sort Zhao, Qi
collection PubMed
description The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.
format Online
Article
Text
id pubmed-10525435
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-105254352023-09-28 Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model Zhao, Qi Liu, Fang Song, Yide Fan, Xiaoya Wang, Yu Yao, Yudong Mao, Qian Zhao, Zheng Bioengineering (Basel) Article The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation. MDPI 2023-08-30 /pmc/articles/PMC10525435/ /pubmed/37760126 http://dx.doi.org/10.3390/bioengineering10091024 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Zhao, Qi
Liu, Fang
Song, Yide
Fan, Xiaoya
Wang, Yu
Yao, Yudong
Mao, Qian
Zhao, Zheng
Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
title Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
title_full Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
title_fullStr Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
title_full_unstemmed Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
title_short Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
title_sort predicting respiratory rate from electrocardiogram and photoplethysmogram using a transformer-based model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525435/
https://www.ncbi.nlm.nih.gov/pubmed/37760126
http://dx.doi.org/10.3390/bioengineering10091024
work_keys_str_mv AT zhaoqi predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT liufang predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT songyide predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT fanxiaoya predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT wangyu predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT yaoyudong predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT maoqian predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel
AT zhaozheng predictingrespiratoryratefromelectrocardiogramandphotoplethysmogramusingatransformerbasedmodel