A patient-adaptable ECG beat classifier using a mixture of experts approach.
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.
Hu
Y H
YH
Department of Electrical and Computer Engineering, University of Wisconsin, Madison 53706, USA. hu@engr.wisc.edu
Palreddy
S
S
Tompkins
W J
WJ
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