D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detection. (April 2023)

• Record Type: Journal Article
• Authors: Zhang, Peng; Ma, Chenbin; Song, Fan; Sun, Yangyang; Feng, Youdan; He, Yufang; Zhang, Tianyi; Zhang, Guanglei
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Atrial fibrillation -- Deep learning -- Dual-domain attention -- Interpretable; 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.2023.104615
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The D2AFNet can exploit the channel-spatial and time series features to mine discriminative atrial fibrillation patterns. The D2AFNet can profoundly explore the different contributions of spatial and temporal segments for excellent interpretation. The D2AFNet method is tenfold cross-validated on the CPSC 2018 dataset and independently tested on the MIT-BIH dataset. The D2AFNet method achieves the accuracies of 99.49% and 99.28% in the two-class and three-class AF detection tasks. Abstract: Atrial fibrillation is one of the common and potentially dangerous persistent cardiac arrhythmias that are generally associated with the risk of stroke and heart failure. Manual electrocardiography diagnosis is the gold standard for the clinical detection of atrial fibrillation, but it has some drawbacks, such as being time-consuming and prone to misclassification due to inter-patient variability. Due to the powerful ability of deep learning to learn and extract rich features from huge datasets, end-to-end deep learning models are generally designed to detect abnormal atrial fibrillation signals automatically. However, these approaches usually ignore the key factors that feature maps from different channels and sequences may contribute differently to atrial fibrillation detection, making it challenging to implement accurate and interpretable models with better generalization performance. To tackle this challenge, we develop a dual-domain attention cascade D2AFNet for accurate and interpretable atrial fibrillation detection by cascading attention-based bidirectional gated recurrent units and densely connected networks embedded with channel-spatial information fusion modules. The D2AFNet can take full advantage of channel-spatial features to enhance the feature representation in the spatial domain, and then combine with the time series features in the temporal domain to form spatial–temporal fusion attention mechanisms to mine discriminative atrial fibrillation patterns. Besides, the D2AFNet can profoundly explore the different contributions of different spatial and temporal segments of feature maps for excellent interpretation. The proposed D2AFNet method is performed ten-fold cross-validation on the publicly available CPSC 2018 dataset, and achieves the accuracies of 99.49% and 99.28% in the two-class and three-class classification tasks, outperforming cutting-edge atrial fibrillation detection methods. In addition, the powerful generalization performance and inference efficiency of the D2AFNet method are also proved on another publicly available MIT-BIH dataset. The advantages of high performance and interpretability indicate that the D2AFNet method has huge potential in the computer-aided diagnosis of atrial fibrillation.