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

被引：393

作者：

Hu, Yu Hen ^{[1
,2
]}

Palreddy, Surekha ^{[2
]}

Tompkins, Willis J. ^{[1
,2
]}

机构：

[1] IEEE

[2] Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI 53706, United States

来源：

| 1997年 / IEEE, Piscataway, NJ, United States卷 / 44期

关键词：

Computer aided diagnosis - Database systems - Neural networks - Patient monitoring;

D O I：

10.1109/10.623058

中图分类号：

学科分类号：

摘要：

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.

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