Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process. (January 2020)

• Record Type: Journal Article
• Authors: Wang, Jibin; Wang, Ping; Wang, Suping
• Is Part Of: Biomedical signal processing and control. Volume 55(2020)
• Publication Date: 2020-01
• Subjects: 92R20 -- 97M60; Electrocardiogram -- Atrial fibrillation -- Wavelet packet transform -- Correlation function -- Artificial neural network classifier; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101662
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The detection of some key parameters is not need. The scheme has more solid and reliable medical basis. It can be considered as an essential reference for physicians to accurately and quickly diagnose AF. It is a first attempt to combine machine learning technology with the correlation function in random process theory for AF detection. The method does not need the complicated hyper-parameter tuning and high hardware facilities such as GPU. Abstract: 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.