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Classifying multichannel ECG patterns with an adaptive neural network

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5 Author(s)
Barro, S. ; Dept. of Electron. & Comput., Santiago de Compostela Univ., Spain ; Fernandez-Delgado, M. ; Vila-Sobrino, J.A. ; Regueiro, C.V.
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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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Engineering in Medicine and Biology Magazine, IEEE  (Volume:17 ,  Issue: 1 )