Abstract

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

Highlights

  • Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality

  • AF is confirmed based on 12-lead electrocardiogram (ECG) findings; it is difficult to identify AF, especially paroxysmal AF (PAF), from ECGs acquired during normal sinus rhythm (NSR) because of low detection by conventional methods and the silent nature of P­ AF4

  • The artificial intelligence (AI)-deep learning algorithm developed to estimate the probability of PAF during NSR using a 12-lead ECG was excellent for identifying PAF

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Summary

Introduction

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. AF is confirmed based on 12-lead electrocardiogram (ECG) findings; it is difficult to identify AF, especially paroxysmal AF (PAF), from ECGs acquired during normal sinus rhythm (NSR) because of low detection by conventional methods and the silent nature of P­ AF4. Conventional methods, such as Holter ECG monitoring and event recorder examination, rely on the detection of symptoms over a relatively short period. We hypothesized that we could identify the subtle ECG changes present in a standard 12-lead ECG during NSR in patients with PAF using a deep learning algorithm. We trained, validated, and tested a recurrent neural network (RNN) deep learning algorithm using NSR ECGs in PAF and healthy individuals in a tertiary hospital

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