title: Patient-specific ventricular beat classification without patient-specific expert knowledge: a transfer learning approach.
abstract: We present an adaptive binary classification algorithm, based on transductive transfer learning. We illustrate the method in the context of electrocardiogram (ECG) analysis. Knowledge gained from a population of patients is automatically adapted to patients' records to accurately detect ectopic beats. On patients from the MIT-BIH Arrhythmia Database, we achieve a median sensitivity of 94.59% and positive predictive value of 96.24%, for the binary classification task of separating premature ventricular contractions (PVCs), a type of ectopic beat, from non-PVCs.
authors.affiliation: Department of Electrical Engineering and Computer Science at MIT. jwiens@csail.mit.edu
authors.forename: Jenna
authors.initials: J
authors.lastname: Wiens
authors.forename: John V
authors.initials: JV
authors.lastname: Guttag
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018879:Ventricular Premature Complexes / Q000175:diagnosis*