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PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.
Shuzan, Md Nazmul Islam; Chowdhury, Moajjem Hossain; Alam, Saadia Binte; Reaz, Mamun Bin Ibne; Khan, Muhammad Salman; Murugappan, M; Chowdhury, Muhammad E H.
Afiliación
  • Shuzan MNI; Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia.
  • Chowdhury MH; Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar.
  • Alam SB; Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia.
  • Reaz MBI; Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar.
  • Khan MS; Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, 1229, Bangladesh.
  • Murugappan M; Department of Electrical and Electronic Engineering, Independent University, Bangladesh (IUB), Dhaka, 1229, Bangladesh.
  • Chowdhury MEH; Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar.
Phys Eng Sci Med ; 2024 Sep 17.
Article en En | MEDLINE | ID: mdl-39287773
ABSTRACT
Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Eng Sci Med Año: 2024 Tipo del documento: Article País de afiliación: Malasia Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Eng Sci Med Año: 2024 Tipo del documento: Article País de afiliación: Malasia Pais de publicación: Suiza