The Mixture of Experts (MOE) approach, combining a global classifier with a patient-specific local classifier trained on 5 minutes of data, significantly enhanced ECG beat classification performance.
Does a mixture-of-experts approach combining global and patient-specific classifiers improve ECG beat classification accuracy compared to a global classifier alone?
A mixture-of-experts approach combining a global ECG classifier with a brief patient-specific local classifier significantly improves automated arrhythmia detection accuracy.
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 et al. (1997) studied Arrhythmia (n=33). Mixture of Experts (MOE) ECG beat classifier vs. Global Expert (GE) classifier was evaluated on ECG beat classification performance. The Mixture of Experts (MOE) approach, combining a global classifier with a patient-specific local classifier trained on 5 minutes of data, significantly enhanced ECG beat classification performance.