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

IEEE Transactions on Biomedical Engineering · 1997 · Vol. 44(9) · pp. 891–900

Yu Hen Hu✉(University of Wisconsin System)Shubha Deepti Palreddy(University of Wisconsin–Madison)W.J. Tompkins(University of Wisconsin–Madison)

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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.

ECG Monitoring and Analysis Analog and Mixed-Signal Circuit Design EEG and Brain-Computer InterfacesClassifier (UML)Computer scienceArtificial intelligenceBeat (acoustics)Pattern recognition (psychology)Machine learningSpeech recognition

MeSH terms

Adaptation, PhysiologicalAlgorithmsArrhythmias, CardiacElectrocardiographyFeasibility StudiesHumansPredictive Value of TestsSensitivity and SpecificitySignal Processing, Computer-AssistedNeural Networks, Computer