Classifying multichannel ECG patterns with an adaptive neural network

In this article the authors describe the application of a new artificial neural network model aimed at the morphological classification of heartbeats detected on a multichannel ECG signal. They emphasize the special characteristics of the algorithm as an adaptive classifier with the capacity to dyna...

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Bibliographic Details
Published inIEEE engineering in medicine and biology magazine Vol. 17; no. 1; pp. 45 - 55
Main Authors Barro, S., Fernandez-Delgado, M., Vila-Sobrino, J.A., Regueiro, C.V., Sanchez, E.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.01.1998
Subjects
Adaptive algorithms
Adaptive signal detection
Adaptive systems
Algorithms
Arrhythmias, Cardiac - physiopathology
Artificial neural networks
Cardiovascular system
Databases as Topic
Electrocardiography
Electrocardiography - classification
Electrophysiology
Humans
Learning systems
Morphology
Neural networks
Neural Networks (Computer)
Noise generators
Noise level
Patient monitoring
Pattern Recognition, Automated
Signal processing
Signal Processing, Computer-Assisted
Software Design
Software Validation
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Summary:In this article the authors describe the application of a new artificial neural network model aimed at the morphological classification of heartbeats detected on a multichannel ECG signal. They emphasize the special characteristics of the algorithm as an adaptive classifier with the capacity to dynamically self-organize its response to the characteristics of the ECG input signal. They also present evaluation results based on traces from the MIT-BIH arrhythmia database.
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ISSN:0739-5175
DOI:10.1109/51.646221