Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolution Neural Networks

Xu, Xiaoyan, Wei, Shoushui, Ma, Caiyun, Luo, Kan, Zhang, Li and Liu, Chengyu (2018) Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolution Neural Networks. Journal of Healthcare Engineering, 2018. p. 2102918. ISSN 2040-2295

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Abstract

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1-s electrocardiogram (ECG) segments to time-frequency images, then the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp) and the area under ROC curve (AUC) results are 74.96%, 86.41% and 0.88. When excluding an extreme poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp and AUC values of 79.05%, 89.99% and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.

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