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7216207 
Journal Article 
Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process 
Wang, J; Wang, P; Wang, S; , 
2020 
Yes 
Biomedical Signal Processing and Control
ISSN: 1746-8094 
ELSEVIER SCI LTD 
OXFORD 
WOS:000502893200027 
Atrial fibrillation (AF) is a common cardiac arrhythmia in clinic. The traditional AF detection using visual inspection by trained physicians is an inefficient and burdensome task. In this paper, we introduce a novel method for the automated AF detection using two-lead electrocardiogram (ECG) signals. We use the wavelet packet transform (WPT) and the correlation function of random process theory to devise an efficient feature extraction strategy for physiological signal analysis, and construct the corresponding histogram. Then, multivariate statistical features based on the correlation among wavelet coefficient series are extracted from the corresponding histogram as the feature set, which is the input to artificial neural network (ANN) classifier for the detection. Moreover, various statistical analyses are performed and some parameter tuning strategies are formulated by fitting the receiver operating characteristic (ROC) curve to ensure the reliability and robustness of the work. To evaluate the classification performance of the algorithm, 10-fold cross-validation is implemented on the MIT-BIH AF database. Compared with some state-of-the-art algorithms, the numerical results prove that our proposed strategy yields superior classification performance. To the best of our knowledge, this is also the first application of random process theory for AF detection, providing great potential in medical diagnosis. (C) 2019 Elsevier Ltd. All rights reserved.