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Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling
Second AMA-IEEE Medical Technology Conference on Delivering on the Promise of Cost Effective Quality Healthcare
  • Mirela Ovreiu, Cleveland Clinic Foundation
  • Marc Petre, Cleveland Clinic Foundation
  • Daniel J. Simon, Cleveland State University
  • Daniel Sessler, Cleveland Clinic Foundation
  • C Allen Bashour, Cleveland Clinic Foundation
Document Type
Conference Proceeding
Publication Date
10-1-2011
Abstract
ATRIAL FIBRILLATION (AF) is the most common cardiac arrhythmia. In the United States alone, it affects more than 2.5 million people annually. The onset of AF is frequently associated with thoracic surgery and it is estimated to occur in 25% of patients that undergo cardiac surgery. The AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology [3]. A valid question regarding the availability of a time lag that could be used to provide adequate treatment against AF onset was raised by Dr. Lombardi in his editorial [1]. We are using a hybrid neuro-fuzzy prediction model that exploits non-linear interactions between ECG parameters. The techniques are non-invasive and analyze 5-lead ECG waveforms. This will allow the model to be easily applied in a Cardio-Vascular Intensive Care Unit setting with very few modifications. http://ama-ieee.embs.org/2011conf/wp-content/uploads/2011/10/AMA_IEEE_2011_Ovreiu_AF_prediction.pdf
Citation Information
M. Ovreiu, M. Petre, D. Simon, D. Sessler, and C. Bashour. (2011). "Prediction of Paroxysmal Atrial Fibrillation Onset in Postoperative Patients Using Neuro-Fuzzy Modeling." Second AMA-IEEE Medical Technology Conference on Delivering on the Promise of Cost Effective Quality Healthcare.