Diagnosis of atrial fibrillation based on unsupervised domain adaptation

In recent years, the proportion of the elderly in the society is continuously increasing. Cardiovascular disease is a big problem that puzzles the health of the elderly. Among them, atrial fibrillation is one of the most common arrhythmia diseases in recent years, which poses a great threat to human life safety. At the same time, deep learning has become a powerful tool for medical and healthcare applications due to its high accuracy and fast detection speed. The diagnosis of atrial fibrillation is based on electrocardiogram, ECG) timing signals. At pre-sent, the scale of the open ECG data set is limited, and a large amount of labeled ECG data is needed to build a high-precision diagnostic model. In this study, a two-channel network model and a feature queue technique are proposed. A high-quality classification diagnosis model of atrial fibrillation is obtained by unsupervised domain adaptive technique, which uses a small amount of labeled data and a large amount of unlabeled data for training. The research content of this paper includes the following aspects: 1) Build a dual-channel network model, which can analyze ECG signals from different feature dimensions. At the same time, the dual-channel output also improves the reliability of the model's pseudo-label in the adaptive training stage and the accuracy of the output in the testing stage. 2) Innovative feature queue technology including global centroid is proposed to participate in the process of domain discrepancy metric calculation, which can use a small amount of labeled data and a large amount of unlabeled data to achieve a more stable and rapid update of the network. 3) Improved and innovated the domain discrepancy metric function, and introduced an evaluation formula for the credibility of false labels to improve the learning efficiency of unlabeled data. Finally, the experimental results show that the proposed two-channel network model and the feature queue technique with global centroid can achieve a high generalization and high precision depth network model by training with a small amount of labeled data and a large amount of unlabeled data. 4) The proposed model achieved a precision of 95.12%, a recall of 95.36%, an accuracy of 98.05%, and an F1 score of 95.23% in the MIT-BIH Arrhythmia Database. In the MIT-BIH Atrial Fibrillation Database, the model achieved a precision of 98.9%, a recall of 99.03%, an accuracy of 99.13%, and an F1 score of 99.08%.