title: A patient-adaptable ECG beat classifier using a mixture of experts approach.
abstract: We present a "mixture-of-experts" (MOE) approach to develop customized electrocardiogram (ECG) beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. A small customized classifier is developed based on brief, patient-specific ECG data. It is then combined with a global classifier, which is tuned to a large ECG database of many patients, to form a MOE classifier structure. Tested with MIT/BIH arrhythmia database, we observe significant performance enhancement using this approach.
authors.affiliation: Department of Electrical and Computer Engineering, University of Wisconsin, Madison 53706, USA. hu@engr.wisc.edu
authors.forename: Y H
authors.initials: YH
authors.lastname: Hu
authors.forename: S
authors.initials: S
authors.lastname: Palreddy
authors.forename: W J
authors.initials: WJ
authors.lastname: Tompkins
mesh_terms: D000222:Adaptation, Physiological; D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis; D004562:Electrocardiography; D005240:Feasibility Studies; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted