Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence

Background: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model can predict a previous or future ventricular fibrillation (VF) episode from a Brugada ECG.Methods and Results: We developed an AI-enabled algorithm using a convolutional neural network. From 157 patients with sus-pected Brugada syndrome, 2,053 ECGs were obtained, and the dataset was divided into 5 datasets for cross-validation. In the ECG-based evaluation, the precision, recall, and F1 score were 0.79 & PLUSMN;0.09, 0.73 & PLUSMN;0.09, and 0.75 & PLUSMN;0.09, respectively. The average area under the receiver-operating characteristic curve (AUROC) was 0.81 & PLUSMN; 0.09. On per-patient evaluation, the AUROC was 0.80 & PLUSMN;0.07. This model predicted the presence of VF with a precision of 0.93 & PLUSMN;0.02, recall of 0.77 & PLUSMN;0.14, and F1 score of 0.81 & PLUSMN;0.11. The negative predictive value was 0.94 & PLUSMN;0.11 while its positive predictive value was 0.44 & PLUSMN;0.29. Conclusions: This proof-of-concept study showed that an AI-enabled algorithm can predict the presence of VF with a substantial performance. It implies that the AI model may detect a subtle ECG change that is undetectable by humans.

Automated summary

In this study, an artificial intelligence model was developed to predict previous or future ventricular fibrillation episodes from Brugada syndrome ECGs. The model achieved high accuracy and F1 score in ECG evaluation and showed good performance in predicting the presence of ventricular fibrillation.