Classification of atrial fibrillation episodes from sparse electrocardiogram data.
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.


METHODS
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, Nonterminating (N), Soon (<1 minute) to be terminating (S), and Terminating immediately (<1 second) (T).


RESULTS
A continuous wavelet transform whose basis functions match the EKG patterns was found to yield compact representation (approximately 2 orders of magnitude). This facilitates the development of efficient algorithms for beat detection, QRST subtraction, and multiple ECG quantifier extraction (eg, QRS width, QT interval). A compact feature set was extracted through principal component analysis of these quantifiers. Accuracies exceeding 90% for AF episode classification were achieved.


CONCLUSIONS
A wavelet representation customized to the ECG signal pattern was found to yield 98% lower entropies compared with other representations that use standard library wavelets. The Classification and Regression Tree (CART) technique seems to distinguish the N vs T, and the S vs T classifications very accurately.
Bukkapatnam
Satish
S
Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, USA.
Komanduri
Ranga
R
Yang
Hui
H
Rao
Prahalad
P
Lih
Wen-Chen
WC
Malshe
Milind
M
Raff
Lionel M
LM
Benjamin
Bruce
B
Rockley
Mark
M
D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity