Why the study?
Atrial fibrillation carries high disability and mortality rates, making early detection and treatment of great clinical significance, though single-feature detection methods suffer from low accuracy.
A novel DCCA-based feature fusion method for automated AF detection from single-lead ECGs demonstrated high accuracy and F1 scores, suggesting potential utility for early AF screening.
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May support AF screening from short single-lead ECGs via DCCA fusion; hypothesis-generating and requires prospective validation.
Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.
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Shi et al. (2021) studied this question.
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