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  3. A Novel Respiratory Rate Estimation Algorithm From Photoplethysmogram Using Deep Learning Model.
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  3. A Novel Respiratory Rate Estimation Algorithm From Photoplethysmogram Using Deep Learning Model.

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

Wee Jian Chin1,2, Ban-Hoe Kwan1,2, Wei Yin Lim3

  • 1Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia.

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|February 10, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study presents a new method for accurately estimating respiratory rate (RR) from photoplethysmogram (PPG) signals using a smaller data window. The novel approach achieves reliable RR estimation with reduced computational needs.

Keywords:
deep learningneural networkphotoplethysmogramrespiratory rate

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Area of Science:

  • Biomedical Engineering
  • Physiological Monitoring
  • Signal Processing

Background:

  • Respiratory rate (RR) is a crucial vital sign for diagnosing conditions like pneumonia.
  • Manual RR counting is often inaccurate and inconsistent.
  • Existing automated RR estimation methods require large data windows or lack precision.

Purpose of the Study:

  • To develop a novel, continuous RR estimation method using photoplethysmogram (PPG) signals.
  • To achieve accurate RR estimation with reduced window sizes and processing demands.
  • To compare the performance of classical and deep learning algorithms for RR estimation.

Main Methods:

  • Utilized the BIDMC and CapnoBase datasets for algorithm evaluation.
  • Employed the Respiratory Rate Estimation (RRest) toolbox.
  • Developed a deep learning model with convolutional and long short-term memory layers.
  • Evaluated classical signal processing techniques and deep learning models.

    • Main Results:

    • Optimal classical methods achieved a Mean Absolute Error (MAE) of 1.9 breaths/min on the BIDMC dataset.
    • The novel deep learning model, using a 7s window, achieved an MAE of 2 breaths/min.
    • The proposed model outperformed other deep learning algorithms using larger window sizes (16s, 32s, 64s).

    Conclusions:

    • Continuous RR estimation from PPG signals is feasible with reduced window sizes.
    • The developed deep learning model offers a promising, computationally efficient approach for RR monitoring.
    • Further research in signal processing can potentially improve PPG-based RR estimation accuracy.