Multi-label correlation guided feature fusion network for abnormal ECG diagnosis

Electrocardiographic (ECG) abnormalities are the most intuitive manifestation in the clinical diagnosis of cardiovascular disease. Although significant progress has been achieved in diagnosing ECG abnormalities, current convolutional neural network (CNN) based methods still show a relatively high rate of misdiagnosis. On the one hand, the ECG data an abnormality imbalance problem. On the other hand, the previous work did not consider the multi-label correlations of the ECG abnormalities. The main objective of this paper is to design an ECG abnormal event detection model based on multi-label correlation guided feature fusion. First, the proposed method calculates the correlation of different ECG abnormality labels on the basis of their frequency and Bayesian conditional probability and divides the ECG abnormalities into groups of different levels. The first-level ECG abnormalities are those that have the highest frequency and the second-level abnormalities are those that have a higher probability of other ECG abnormalities given the first-level ECG abnormalities, and so on. Then, the feature fusion strategy uses the multi-label correlation to guide the fusion of features of different levels. Specifically, the features of the first-level ECG signals are integrated into the features of the second-level ECG signals and the features of the former two-level ECG signals are integrated into the features of the third-level ECG signals. Furthermore, a multi-scale receptive field fusion module is proposed to enhance the multi-scale representation ability of the features. To evaluate the performance of the proposed method, it is validated against the 1st China physiological signal challenge dataset, and it achieved better results compared with other state-of-the-art methods. This proposed method achieves a precision score of 0.816, a sensitivity score of 0.845 and an F1-score of 0.827, which are better than other methods. The comparative analysis shows that the proposed method demonstrates the effectiveness in abnormal ECG diagnosis.