Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings. (December 2021) - Zhang, Peng; Ma, Chenbin; Sun, Yangyang; Fan, Guangda; Song, Fan; Feng, Youdan; Zhang, Guanglei
Abstract: Background and objective: Atrial fibrillation (AF) is the most common persistent cardiac arrhythmia in clinical practice, and its accurate screening is of great significance to avoid cardiovascular diseases (CVDs). Electrocardiogram (ECG) is considered to be the most commonly used technique for detecting AF abnormalities. However, previous ECG-based deep learning algorithms did not take into account the complementary nature of inter-layer information, which may lead to insufficient AF screening. This study reports the first attempt to use hybrid multi-scale information in a global space for accurate and robust AF detection. Methods: We propose a novel deep learning classification method, namely, global hybrid multi-scale convolutional neural network (i.e., GH-MS-CNN), to implement binary classification for AF detection. Unlike previous deep learning methods in AF detection, an ingenious hybrid multi-scale convolution (HMSC) module, for the advantage of automatically aggregating different types of complementary inter-layer multi-scale features in the global space, is introduced into all dense blocks of the GH-MS-CNN model to implement sufficient feature extraction, and achieve much better overall classification performance. Results: The proposed GH-MS-CNN method has been fully validated on the CPSC 2018 database and tested on the independent PhysioNet 2017 database. The experimental results show that the global and hybrid multi-scale information has tremendous advantages over local and single-type multi-scale information in AF screening. Furthermore, the proposed GH-MS-CNN method outperforms the state-of-the-art methods and achieves the best classification performance with an accuracy of 0.9984, a precision of 0.9989, a sensitivity of 0.9965, a specificity of 0.9998 and an F1 score of 0.9954. In addition, the proposed method has achieved comparable and considerable generalization capability on the PhysioNet 2017 database. Conclusions: The proposed GH-MS-CNN method has promising capabilities and great advantages in accurate and robust AF detection. It is assumed that this research has made significant improvements in AF screening and has great potential for long-term monitoring of wearable devices. Highlights: A profitable HMSC module is designed to extract complementary information from different types of convolution layers. The proposed method can automatically capture and integrate the discriminative multi-scale features in the global space. This method achieves the best classification performance and considerable generalization capability in single-lead ECG recordings. Extensive and elaborate experiments have effectively demonstrated the effectiveness and feasibility of the proposed method. The proposed method has potential in computer-aided AF diagnosis based on intelligent wearable devices.