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

Presents a "mixture-of-experts" (MOE) approach to develop customized electrocardiogram (EGG) 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-specifi...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 1997-09, Vol.44 (9), p.891-900
Main Authors: Yu Hen Hu, Palreddy, S., Tompkins, W.J.
Format: Article
Language:English
Subjects:
Adaptation, Physiological
Algorithms
Arrhythmias, Cardiac - diagnosis
Biological and medical sciences
Cardiovascular system
Classification algorithms
Computerized monitoring
Electrocardiography
Feasibility Studies
Frequency
Humans
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Medical services
Neural networks
Neural Networks (Computer)
Predictive Value of Tests
Real time systems
Sensitivity and Specificity
Signal processing algorithms
Signal Processing, Computer-Assisted
Testing
Transaction databases
Ultrasonic investigative techniques
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Summary:Presents a "mixture-of-experts" (MOE) approach to develop customized electrocardiogram (EGG) 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, the authors observe significant performance enhancement using this approach.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.623058