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

被引：386
|
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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