A PATIENT-ADAPTABLE ECG BEAT CLASSIFIER USING A MIXTURE OF EXPERTS APPROACH

We present a ''mixture-of-experts'' (MOE) approach to develop customiz ed electrocardiogram (ECG) beat classifier in an effort to further imp rove the performance of ECG processing and to offer individualized hea lth care. A small customized classifier is developed based on brief, p atient-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 M OE classifier structure. Tested with MIT/BIH arrhythmia database, we o bserve significant performance enhancement using this approach.