Abstract-We present a "mixture-of-experts" (MOE) approach to develop customized electrocardigram (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.Index Terms-ECG beat classification, MIT/BIH database, mixture of experts, neural network, patient adaptation.

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