Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity

BMC Medical Informatics and Decision Making

Daniel J. Clauw¹  Zhi Li²  Jonathan Gryak³  Kayvan Najarian⁴  Sangeeta Lathkar-Pradhan⁵  Hakan Oral⁵  Hamid Ghanbari⁵  Brahmajee K. Nallamothu⁵  Kevin M. Wheelock⁶  Pujitha Gunaratne⁷ 

[1] Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA;Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;Department of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA;Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA;Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA;Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA;Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA;Department of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA;Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA;Toyota Motor North America, Ann Arbor, MI, USA;

关键词: Machine learning;    Atrial fibrillation;    Arrhythmia prediction;    Signal processing;    Probabilistic finite-state automata;   

DOI  :  10.1186/s12911-021-01723-3

来源: Springer

【 摘 要 】

BackgroundRapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity.MethodsThis study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity.ResultsAmong the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67–0.78.ConclusionThe DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

【 授权许可】

CC BY