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Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques

Saidul Islam, Gaith Rjoub, Hanae Elmekki, Jamal Bentahar, Witold Pedrycz, Robin Cohen

TL;DR

This survey paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings.

Abstract

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.

Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques

TL;DR

This survey paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings.

Abstract

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.

Paper Structure

This paper contains 18 sections, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Historical Background
  4. Historical Techniques
  5. Modern Advancements
  6. CPR Methodologies and AI-Driven Approaches
  7. Methodologies
  8. AI-Driven Approaches
  9. Classification of ML Approaches for CPR Tasks
  10. Taxonomy
  11. Rhythm Analysis
  12. Outcome Prediction
  13. Non-Invasive Blood Pressure and Chest Compression
  14. Pulse and Return Of Spontaneous Circulation Detection (ROSC)
  15. Future Prospects, Challenges and Discussion
  16. ...and 3 more sections

Figures (9)

  • Figure 1: Visual Abstract
  • Figure 2: Evolution of published CPR documents over the years.
  • Figure 3: Number of CPR-related documents over the years from various sources.
  • Figure 4: Document contributions from various subject areas to CPR research
  • Figure 5: Documents published per year showing the growing interest and adoption of AI and ML in CPR research
  • ...and 4 more figures