A patient-adaptable ECG beat classifier using a mixture of experts approach

Y H Hu; S Palreddy; W J Tompkins

doi:10.1109/10.623058

A patient-adaptable ECG beat classifier using a mixture of experts approach

IEEE Trans Biomed Eng. 1997 Sep;44(9):891-900. doi: 10.1109/10.623058.

Authors

Y H Hu¹, S Palreddy, W J Tompkins

Affiliation

¹ Department of Electrical and Computer Engineering, University of Wisconsin, Madison 53706, USA. hu@engr.wisc.edu

PMID: 9282481
DOI: 10.1109/10.623058

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.

MeSH terms

Adaptation, Physiological
Algorithms
Arrhythmias, Cardiac / diagnosis
Electrocardiography*
Feasibility Studies
Humans
Neural Networks, Computer*
Predictive Value of Tests
Sensitivity and Specificity
Signal Processing, Computer-Assisted*