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Extracting ventilatory waveforms from screen recordings: a validated image processing methodology and its application

Ivan Ruiz1, Guillermo Jaramillo2, Jose I Garcia2

  • 1Universidad Santiago de Cali, Cl. 5 #62 -00, Cali, 760035, Colombia.

Biomedical Physics & Engineering Express
|July 15, 2026
PubMed
Summary

This study presents an accessible image processing method to capture high-fidelity ventilatory data from ventilator screens. This novel approach enables new predictive models for respiratory mechanics, even detecting breathing asynchrony.

Keywords:
Inverse modellingexpiratory parametersimage processingmodel-based methodssingle compartment model

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Area of Science:

  • Respiratory Mechanics
  • Biomedical Engineering
  • Image Processing Applications

Background:

  • High-fidelity ventilatory waveform data is crucial for respiratory mechanics research but often inaccessible, especially in resource-limited settings.
  • Existing methods for data acquisition can be costly and complex, hindering widespread research and clinical application.

Purpose of the Study:

  • To introduce and validate a novel, accessible framework for acquiring high-fidelity ventilatory data using image processing of ventilator screen recordings.
  • To develop and evaluate a predictive Single Compartment Model (SCM) for estimating expiratory parameters from inspiratory data.
  • To explore the potential of this framework and model in understanding respiratory mechanics and detecting breathing patterns.

Main Methods:

  • Image processing techniques were applied to ventilator screen recordings to extract ventilatory waveform data.
  • The accuracy and reliability of the image-derived data were validated against sensor-derived data from a laboratory emulator.
  • A novel predictive Single Compartment Model (SCM) was developed incorporating auxiliary parameters (L and H) to estimate expiratory airway resistance.

Main Results:

  • The image processing framework demonstrated high fidelity (R^2 > 0.99) compared to sensor data.
  • Image-derived patient data successfully reproduced established correlations in respiratory mechanics.
  • The predictive SCM accurately estimated expiratory parameters in sedated patients but could not capture spontaneous breathing dynamics and asynchrony.

Conclusions:

  • Image processing offers a reliable and accessible method for ventilatory data acquisition, suitable for diverse research settings.
  • The validated framework supports the development of new predictive tools for respiratory mechanics.
  • The model's failure to capture breathing asynchrony suggests its potential as a non-invasive indicator for detecting such events.