Predicting Postoperative Atrial Fibrillation from Independent ECG Components


  • Chih-Chun Chia University of Michigan, Ann Arbor
  • James Blum University of Michigan Hospital
  • Zahi Karam University of Michigan
  • Satinder Singh University of Michigan
  • Zeeshan Syed University of Michigan



atrial fibrillation, independent components, medicine


Postoperative atrial fibrillation (PAF) occurs in 10% to 65% of the patients undergoing cardiothoracic surgery. It is associated with increased post-surgical mortality and morbidity, and results in longer and more expensive hospital stays. Accurately stratifying patients for PAF allows for selective use of prophylactic therapies (e.g., amiodarone). Unfortunately, existing tools to stratify patients for PAF fail to provide clinically adequate discrimination. Our research addresses this situation through the development of novel electrocardiographic(ECG) markers to identify patients at risk of PAF. As a first step, we explore an eigen-decomposition approach that partitions ECG signals into atrial and ventricular components by exploiting knowledge of the underlying cardiac cycle. We then quantify electrical instability in the myocardium manifesting as probabilistic variations in atrial ECG morphology to assess therisk of PAF. When evaluated on 385 patients undergoing cardiac surgery, this approach of stratifying patients for PAF through an analysis of morphologic variability within decoupled atrial ECG demonstrated substantial promise and improved net reclassification by over 53% relative to the use of baseline clinical characteristics.




How to Cite

Chia, C.-C., Blum, J., Karam, Z., Singh, S., & Syed, Z. (2014). Predicting Postoperative Atrial Fibrillation from Independent ECG Components. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Machine Learning Applications