Machine learning based atrial fibrillation detection and onset prediction using QT-dynamicity.

Objective
This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction. 
Approach
We studied the importance of QT-dynamicity 1) in the detection and 2) the onset prediction (i.e. forecasting) of paroxysmal AF episodes using gradient-boosted decision trees (GBDT), an interpretable machine learning technique. We labeled 176 paroxysmal AF onsets from 88 patients in our unselected Holter recordings database containing paroxysmal AF episodes. Raw ECG signals were delineated using a wavelet-based signal processing technique. A total of 44 ECG features related to interval and wave durations and amplitude were selected. and the GBDT model was trained with a Bayesian hyperparameters selection for various windows. 
Main results
The mean age of the patients was 75.9±11.9 (range 50-99), the number of episodes per patient was 2.3±2.2 (range 1-11), and CHA2DS2-VASc score was 2.9±1.7 (range 1-9). For the detection of AF, we obtained an area under the receiver operating curve (AUROC) of 0.99 (CI 95% 0.98 - 0.99) using a 30s window. Features related to RR intervals were the most influential, followed by those on QT intervals. For the AF onset forecast, we obtained an AUROC of 0.739 (0.712-0.766) using a 120s window. R wave amplitude and QT dynamicity as assessed by Spearman's correlation of the QT-RR slope were the best predictors. 
Significance
The QT dynamicity can be used to accurately predict the onset of AF episodes. Ventricular repolarization, as assessed by QT dynamicity, adds information that allows for better short time prediction of AF onset, compared to relying only on RR intervals and HRV. Communication between the ventricles and atria is mediated by the autonomic nervous system. The variations in intraventricular conduction and ventricular repolarization changes resulting from the influence of the ANS play a role in the initiation of AF.&#xD.