title: Weighted conditional random fields for supervised interpatient heartbeat classification.
abstract: This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.
authors: theMachine Learning Group, Université Catholique de Louvain, B-1348 Louvain-La-Neuve, Belgium. gael.delannoy@uclouvain.be
authors: Gaël
authors: G
authors: de Lannoy
authors: Damien
authors: D
authors: Francois
authors: Jean
authors: J
authors: Delbeke
authors: Michel
authors: M
authors: Verleysen
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity