P6568Forecasting atrial fibrillation using machine learning techniques

Abstract Background Forecasting atrial fibrillation (AF) a few minutes before its onset has been studied, mainly based on heart rate variability parameters, derived from 24-hour ECG Holter monitorings. However, these studies have shown conflicting, non-clinically applicable results. Nowadays, machine learning algorithms have proven their ability to anticipate events. Therefore, forecasting AF before its onset should be (re)assessed using machine learning techniques. A reliable forecasting could improve results of preventive pacing in patients with cardiac electronic implanted devices (CEID). Purpose To forecast an oncoming AF episode in individual patients using machine learning techniques. To evaluate the effect if the onset of an AF episode can be forecasted on longer time frames. Methods The totality of the raw data of a data set of 10484 ECG Holter monitorings was retrospectively analyzed and all AF episodes were annotated. Onset of each AF episode was determined with a precision of 5 msec. We only took AF events into consideration if they lasted longer than 30 seconds. Of all patients in the dataset, 140 presented paroxysmal AF (286 recorded AF episodes). We only used RR intervals to predict the presence of AF. We developed two different types of machine learning algorithms with different computational power requirements: a “dynamic” deep and recurrent neural net (RNN) and a “static” decision-tree with adaboost (boosting trees) more suitable for embedded devices. These algorithms were trained on one set of patients (around 90%) and tested on the remaining set of patients (around 10%). Results The performance figures are summarized in the table. Both algorithms can be tuned to increase their specificity (at a loss of sensitivity) or vice versa, depending on the objective. Performance of forecasting algorithms RR-distance Boosting trees AUC RNN AUC 30–1 RR-Interval(s) before an AF event 97.1% 98.77% 60–31 RR-Intervals before an AF event 97.5% 99.1% 90–61 RR-Intervals before an AF event 96.9% 99.1% 120–91 RR-Inervals before an AF event 98.2% 98.9% AUC for Area Under ROC Curves. Conclusion Based upon this retrospective study, we show that AF can be forecasted on an individual level with high predictive power using machine learning algorithm, with little drop-off of predictive value within the studied distances (1–120 RR intervals before a potential AF episode). We believe that the embedding of our new algorithm(s) in CEID's could open the way to innovativ