Classification of atrial fibrillation episodes from sparse electrocardiogram data

Abstract Background Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. This paper presents the application of the Classification and Regression Tree (CART) technique for detecting spontaneous termination or sustenance of AF with sparse data. Method Electrocardiogram (ECG) record...

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Veröffentlicht in:	Journal of electrocardiology 2008-07, Vol.41 (4), p.292-299
Hauptverfasser:	Bukkapatnam, Satish, PhD, Komanduri, Ranga, PhD, DEng, Yang, Hui, MS, Rao, Prahalad, MS, Lih, Wen-Chen, PhD, Malshe, Milind, MS, Raff, Lionel M., PhD, Benjamin, Bruce, PhD, Rockley, Mark, PhD
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Atrial fibrillation (AF) Atrial Fibrillation - diagnosis Cardiovascular CART decision tree Computer Simulation Data Interpretation, Statistical Diagnosis, Computer-Assisted - methods Electrocardiogram (ECG) Electrocardiography - methods Feature extraction Humans Models, Cardiovascular Reproducibility of Results Sensitivity and Specificity Statistical analysis Wavelet analysis
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Zusammenfassung:	Abstract Background Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. This paper presents the application of the Classification and Regression Tree (CART) technique for detecting spontaneous termination or sustenance of AF with sparse data. Method Electrocardiogram (ECG) recordings were obtained from the PhysioNet (AF Termination Challenge Database 2004) Web site. Signal analysis, feature extraction, and classification were made to distinguish among 3 AF episodes, namely, N onterminating (N), S oon (
ISSN:	0022-0736 1532-8430
DOI:	10.1016/j.jelectrocard.2008.01.004