# Sinus rhythm maintenance in persistent atrial fibrillation: 12-lead ECG multiscale entropy characterization

## TL;DR

This study shows that multiscale entropy measures from ECG precordial leads can better predict the success of electrical cardioversion in atrial fibrillation patients than traditional methods.

## Contribution

The novel contribution is demonstrating that Refined Multiscale Entropy (MSE) from precordial leads improves ECV outcome prediction accuracy to 87% and up to 98% with machine learning.

## Key findings

- Refined MSE on precordial leads achieved 87% prediction accuracy for ECV outcomes.
- Support vector machines increased prediction accuracy to 98% using MSE features.
- Traditional indices had 79% accuracy but were outperformed by MSE-based methods.

## Abstract

Persistent atrial fibrillation is the most common sustained cardiac arrhythmia, frequently linked with increased mortality and morbidity. Electrical cardioversion (ECV) remains the gold standard for sinus rhythm (SR) restoration, even though presenting potential adverse effects and a high relapsing rate. Predicting ECV outcome from the 12-lead ECG could reduce healthcare costs while preventing complications in patients unlikely to maintain SR. To this end, atrial activity (AA) organization has been traditionally evaluated through the amplitude and dominant frequency of the fibrillatory waves at lead II. However, physiological systems are known to exhibit complex dynamics across multiple time-scales, making multiscale (MSE) entropy measures a more suitable tool, as they can incorporate relevant information that may have been previously overlooked. Here, the predictive power of different MSE-based indices for the ECV outcome in 58 patients is evaluated. AA was estimated using a QT segment cancellation algorithm. Patients were classified based on SR maintenance after a 30-day follow-up. Results show that traditionally used indices report the highest predictive rate over the limb leads (79%). However, they are outperformed by Refined MSE over precordial leads (87%). Moreover, when considering statistical modeling techniques such as support vector machines, the prediction accuracy is increased (98%). In conclusion, MSE-based indices computed from precordial leads can robustly predict ECV outcome with higher accuracy than traditional approaches.