Explainable Paroxysmal Atrial Fibrillation Diagnosis Using Electrocardiogram with Artificial Intelligence

FUNDING ACKNOWLEDGEMENTS: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) BACKGROUND: Paroxysmal atrial fibrillation (PAF) is common among patients with cryptogenic stroke or transient ischemic attack, and has a silent nature. Therefore, robust and reliable early detection of atrial fibrillation would be valuable for managing cardiovascular comorbidities. Therefore, robust and reliable early detection of atrial fibrillation would be valuable for managing cardiovascular comorbidities. Despite many efforts to predict or detect atrial fibrillation, an adequate explanation for estimation remains problematic. METHOD: We present a statistical prediction model employing a deep-learning approach to identify a PAF from normal electrocardiogram (ECG), and interpret their causes. Based on 552,372 12-lead ECG data of 318,321 patients, we show that an AI-based model can capture the features of atrial fibrillation during normal sinus rhythm using ECG and additional characteristics. As a result of applying our labelling method, only 26,541 ECGs (4.8%) were annotated with PAF. We also applied Layer-wise Relevance Propagation(LRP) algorithm to investigate the regions of the ECG that primarily affect the discrimination of PAF. RESULTS: The performance of our model was evaluated as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The AUC for predicting PAF was 0.910. The predictive performance according to the class was 0.837 in the negative prediction and 0.705 in the positive prediction. The sensitivity analysis results showed that the difference in PAF prediction information for each lead was not significant. Among the features obtained from ECG, the PR interval and QT interval were significant explaining factors for the prediction of PAF, and this fact was confirmed by visualizing the ECG data using the LRP algorithm. CONCLUSION: We expect that the discrimination of undetected atrial fibrillation can be used as a pre-emptive assistive tool to provide probabilistic predictions for the screening of PAF and could be enables insights into the cardiac dysfunction. [Figure: see text]