ECG arrhythmia classification based on optimum-path forest

Author:

Luz Eduardo José da S.,Nunes Thiago M.,de Albuquerque Victor Hugo C.,Papa João P.,Menotti David

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference53 articles.

1. Wavelet transforms and the ECG: A review;Addison;Physiological Measurement,2005

2. Allène, C., Audibert, J. Y., Couprie, M., Cousty, J., & Keriven, R. (2007). Some links between min-cuts, optimal spanning forests and watersheds. In Mathematical morphology and its applications to image and signal processing, MCT/INPE (pp. 253–264).

3. Association for the Advancement of Medical Instrumentation (AAMI) (2008). Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. American National Standards Institute, Inc. (ANSI), ANSI/AAMI/ISO EC57, 1998-(R)2008.

4. Analysis and classification of cardiac arrhythmia using ECG signals;Bhardwaj;International Journal of Computer Applications,2012

5. Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network;Ceylan;Expert Systems with Applications,2007

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