title: An adapting system for heartbeat classification minimising user input.
abstract: An adaptive system for the processing of the electrocardiogram (ECG) for the classification of heartbeats into beat classes that seeks to minimize the required input from the user is presented. A first set of beat annotations is produced by the system by processing an incoming recording with a global-classifier. The beat annotations are then ranked by a confidence measure calculated from the posterior probabilities estimates associated with each beat classification. An expert then validates and if necessary corrects a fraction of the least confident beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. Our results show that we can achieve a significant boost in classification performance of the system by using a small number of beats for adaptation.
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
mesh_terms: D000465:Algorithms; D004562:Electrocardiography; D006339:Heart Rate / Q000502:physiology*; D006352:Heart Ventricles / Q000503:physiopathology; D006801:Humans