Atrial Fibrillation Detection Using an Improved Multi-Scale Decomposition Enhanced Residual Convolutional Neural Network

• Published in: IEEE access 2019, Vol.7, p.89152-89161
• Main Authors: Cao, Xin-Cheng, Yao, Bin, Chen, Bin-Qiang
• Format: Article
• Language: English
• Subjects: Accuracy; Arrhythmia; Artificial neural networks; atrial fibrillation; Cardiac arrhythmia; Classification; Convolution; Convolutional neural networks; Datasets; Decomposition; deep learning; Electrocardiogram; Electrocardiography; Feature extraction; Fibrillation; Machine learning; Medical research; Neural networks; residual convolutional neural networks; Rhythm; transfer learning; wavelet frame; Wavelet packets
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2926749

Atrial fibrillation, the most common sustained arrhythmia, is still a big challenge for researchers in the medical field. Many studies attempt to realize intelligent classification of AF based on deep learning methods. However, many of the studies focused on investigations of relatively simple datasets collected from a relatively small number of subjects. On the other hand, sophisticated preprocessing is usually adopted to analyse the ECG signals. These two factors significantly affect the generalization ability of the trained models for complicated data sets collected from a large number of subjects. In order to address this problem, an improved multi-scale decomposition enhanced residual convolutional neural network is proposed. The proposed method is applied to the large single-lead ECG dataset provided by the PhysioNet/CinC Challenge 2017, and good classification accuracy is suggested by the testing results. In the proposed method, the original ECG record with a large difference in length is re-segmented into short samples of 9 s. Then, using the derived wavelet frame decomposition, the segmented short samples are decomposed and reconstituted into sub-signal samples of different scales. We trained the fast down-sampling residual convolutional neural networks (FDResNets) with the original short-signal dataset and the reconstructed dataset of each scale. The transfer learning technique is then applied to couple the three FDResNets with good performance into a multi-scale decomposition enhanced residual convolutional neural network (MSResNet). The FDResNet trained by the [0, 9.375 Hz] reconstruction dataset achieved the best performance. After six-fold cross-validation, the average test accuracy reached 87.12%, and the average comprehensive F1 score reached 85.29%. The average test accuracy of the multi-scale residual neural network reached 92.1%, and the average overall F1 score reached 89.9%.