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Purpose <p>Carotid blood flow (CBF) is a critical indicator during cardiopulmonary resuscitation (CPR), representing blood flow to the brain. In actual clinical settings, measuring it is almost impossible. In this study, we developed and evaluated machine learning models that estimate CBF using biological signals that can be measured during CPR.</p> Methods <p>To simulate various compression conditions in animal experiments, we employed robot manipulators and a commercial automated CPR machine. Five biological signals—arterial blood pressure, central venous pressure, photoplethysmography, intrathoracic pressure, end tidal carbon dioxide (ETCO2)—and CBF were measured. Various features were extracted from these biological signals to estimate CBF. Machine learning models were trained on various combinations of these features.</p> Results <p>The models achieved a classification accuracy of up to 0.97, determining whether the CBF was above or below a predetermined threshold. Furthermore, the most influential biological signal highly correlated with CBF was identified as ETCO2.</p> Conclusion <p>This machine learning model holds great promise for improving treatment efficacy by assessing and providing feedback on CPR quality in clinical settings.</p>

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Determination of cardiopulmonary resuscitation quality based on machine learning algorithms using various biological signals

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Byung Jun Kim,
  • Dong Ah Shin,
  • Woo Sang Cho,
  • Soyoon Kwon,
  • Jung Chan Lee,
  • Taegyun Kim,
  • Kyung Su Kim,
  • Gil Joon Suh,
  • Jaehoon Sim,
  • Jaeheung Park

摘要

Purpose

Carotid blood flow (CBF) is a critical indicator during cardiopulmonary resuscitation (CPR), representing blood flow to the brain. In actual clinical settings, measuring it is almost impossible. In this study, we developed and evaluated machine learning models that estimate CBF using biological signals that can be measured during CPR.

Methods

To simulate various compression conditions in animal experiments, we employed robot manipulators and a commercial automated CPR machine. Five biological signals—arterial blood pressure, central venous pressure, photoplethysmography, intrathoracic pressure, end tidal carbon dioxide (ETCO2)—and CBF were measured. Various features were extracted from these biological signals to estimate CBF. Machine learning models were trained on various combinations of these features.

Results

The models achieved a classification accuracy of up to 0.97, determining whether the CBF was above or below a predetermined threshold. Furthermore, the most influential biological signal highly correlated with CBF was identified as ETCO2.

Conclusion

This machine learning model holds great promise for improving treatment efficacy by assessing and providing feedback on CPR quality in clinical settings.