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

Autor:	Grégoire JM; IRIDIA, Université Libre de Bruxelles, Av. Adolphe Buyl 87, 1050 Bruxelles, Belgium.; Cardiology Department, Université de Mons, Place du Parc 20, 7000 Mons, Belgium., Gilon C; IRIDIA, Université Libre de Bruxelles, Av. Adolphe Buyl 87, 1050 Bruxelles, Belgium., Vaneberg N; IRIDIA, Université Libre de Bruxelles, Av. Adolphe Buyl 87, 1050 Bruxelles, Belgium., Bersini H; IRIDIA, Université Libre de Bruxelles, Av. Adolphe Buyl 87, 1050 Bruxelles, Belgium., Carlier S; Cardiology Department, Université de Mons, Place du Parc 20, 7000 Mons, Belgium.
Jazyk:	angličtina
Zdroj:	Physiological measurement [Physiol Meas] 2024 Jul 01; Vol. 45 (7). Date of Electronic Publication: 2024 Jul 01.
DOI:	10.1088/1361-6579/ad55a1
Abstrakt:	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. The dataset was split into two parts at the patient level, meaning that the recordings from each patient were only present in either the train or test set, but not both. We used 80% on the database for the training and the remaining 20% for the test of the trained model. The model was evaluated using 5-fold cross-validation. 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) and an accuracy of 95% using a 30 s 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) and an accuracy of 74% 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 heart rate variability. Communication between the ventricles and atria is mediated by the autonomic nervous system (ANS). The variations in intraventricular conduction and ventricular repolarization changes resulting from the influence of the ANS play a role in the initiation of AF. (© 2024 Institute of Physics and Engineering in Medicine.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu