Role of artificial intelligence in defibrillators: a narrative review

Abstract

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the 'black-box' phenomenon.

Keywords: Defibrillators, Implantable; Heart Arrest; Tachycardia, Ventricular; Ventricular Fibrillation.

Figure 1

Top-down approach to AI. Machine learning is a type of AI, which can be broadly split into supervised and unsupervised machine learning. We will mainly focus on the use of supervised machine learning techniques in defibrillators. Adapted from Refs. AI, artificial intelligence; ANN, artificial neural network; CNN, convolutional neural networks;

Figure 2

Schematic diagram of a convolutional neural network. Adapted from Ref. AS, aortic stenosis.