Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine.

Journal: Computers in biology and medicine

Published Date: Mar 14, 2015

Abstract

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Automatic detection of AF could substantially help in early diagnosis, management and consequently prevention of the complications associated with chronic AF. In this paper, we propose a novel method for automatic AF detection.

Authors

Shadnaz Asgari

Department of Computer Engineering and Computer Science, California State University, Long Beach, 1250 Bellflower Boulevard-MS 8302, Long Beach, CA 90840, USA. Electronic address: Shadnaz.Asgari@csulb.edu.
Alireza Mehrnia

Department of Electrical Engineering, University of California, Los Angeles, 56-125B Engineering IV Building, Box 951594, Los Angeles, CA 90095, CA.
Maryam Moussavi

Department of Electrical Engineering, California State University, Long Beach, 1250 Bellflower Boulevard, Long Beach, CA 90840, CA.

Keywords

Algorithms Arrhythmias, Cardiac Atrial Fibrillation Diagnosis, Computer-Assisted Electrocardiography Fourier Analysis Humans Reproducibility of Results ROC Curve Sensitivity and Specificity Signal Processing, Computer-Assisted Support Vector Machine Wavelet Analysis

External Resources

View on PubMed Access via DOI PubMed (25817534)

Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine.

Abstract

Authors

Keywords

External Resources

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