Artificial Intelligence in the Diagnosis and Management of Arrhythmias

Venkat D. Nagarajan; Su-Lin Lee; Jan-Lukas Robertus; Christoph A. Nienaber; Natalia A. Trayanova; Sabine Ernst


Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Intracardiac Data and Machine Learning Applications in Atrial Fibrillation

Large quantities of intracardiac data are recorded during EP procedures. Recent advances in ML methodologies have encouraged researchers to apply these techniques to the intracardiac electrograms and EAM data with a view to define extra pulmonary ablation sites in AF.

Schilling et al.[83] showed the feasibility of classifying complex fractionated atrial electrograms in an objective way using fuzzy decision tree algorithm retrospectively on intracardiac electrograms. Atrial electrograms were classified into four subgroups ranging from non-fractionated with high frequency to continuous activity achieving a correct rate of 81 ± 3%. Electrograms with continuous activity were detected correctly 100% of the time.

In a proof of concept study, McGillivray et al.[84] developed random forest supervised ML algorithm to locate re-entrant circuits driving AF using indirect feature measurements, derived from electrograms in a simulated model. The model correctly identified 95.4% of drivers in the simulation model.

In a recent study, Alhusseini et al.[85] developed an ML algorithm to classify intracardiac electrical patterns during AF. They used a convoluted neural network DL approach to analyse EAM data obtained from bi-atrial sites using basket catheters. Spatial maps of activation were created to identify the presence of rotational activation features. Algorithm compared well with a team of experts with an accuracy of 97.3% when the experts were in unanimous agreement and 85.1% in more difficult instances. Convoluted neural network accuracy in the test set was similar for locations containing termination sites (95.6%) or otherwise (94.2%).

With ML-guided ablation strategies becoming a possibility for the near future, electrophysiologists would need real-time access to integrated data from different sources including EAM and 3D cardiac imaging. Availability of this information with the ability to manipulate the data for better visualization whilst still operating in a sterile field would enhance operator dexterity and procedural efficacy during complex ablations. Holographic visualization of real-time catheter position, cardiac geometry, EAM, and ablation data in an EP lab has been shown to be feasible.[86] Systems to provide augmented reality solutions in EP labs are currently being developed for future use[87] and would certainly aid in better and efficient work flow.