Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

• Published in: Computers in biology and medicine Vol. 102; pp. 327 - 335
• Main Authors: Faust, Oliver, Shenfield, Alex, Kareem, Murtadha, San, Tan Ru, Fujita, Hamido, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2018; Elsevier Limited
• Subjects: Accuracy; Algorithms; Artificial intelligence; Atrial fibrillation; Automation; Cardiac arrhythmia; Computer memory; Deep learning; Diagnosis; Feature extraction; Fibrillation; Generalized linear models; Heart; Heart rate; Internal Medicine; Long short-term memory; Machine learning; Methods; Monitoring; Morphology; Neural networks; Other; Quality of life; Recurrent neural network; Recurrent neural networks; Stroke; Deep learning; Heart rate; Recurrent neural network; Atrial fibrillation

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We...