Overview of Machine Learning and Deep Learning Approaches for Detecting Shockable Rhythms in AED in the Absence or Presence of CPR

Sudden Cardiac Arrest (SCA) is one of the leading causes of death worldwide. Therefore, timely and accurate detection of such arrests and immediate defibrillation support for the victim is critical. An automated external defibrillator (AED) is a medical device that diagnoses the rhythms and provides electric shocks to SCA patients to restore normal heart rhythms. Machine learning and deep learning-based approaches are popular in AEDs for detecting shockable rhythms and automating defibrillation. There are some works in the literature for reviewing various machine learning (ML) and deep learning (DL) algorithms for shockable ECG signals in AED. Starting in 2017 and beyond, different DL algorithms were proposed for the AED. This paper provides an overview of AED, including its circuit diagram and application to SCA patients. It also presents the most up-to-date ML and DL approaches for detecting shockable rhythms in AEDs without cardiopulmonary resuscitation (CPR) or during CPR. It also provides a performance comparison of these approaches and discusses other researchers’ results that lay the foundation for researchers to delve in-depth. Furthermore, the research gaps and recommendations for future research provided in this review paper will be helpful to the researchers, scientists, and engineers in conducting further research in this critical field