Key result
A feature fusion-based detection method using artificial and abstract features achieved a mean accuracy of 0.857 and an F1 score of 0.837 for identifying atrial fibrillation from short single-lead ECGs.
Why the study?
Detecting atrial fibrillation from short single-lead ECG recordings with low signal-to-noise ratio is key for wearable heart monitoring systems.
Does a feature fusion method combining artificial and 1-D CNN abstract features accurately detect atrial fibrillation in short single-lead ECGs with low signal-to-noise ratio?
Does a feature fusion method combining artificial and 1-D CNN abstract features accurately detect atrial fibrillation in short single-lead ECGs with low signal-to-noise ratio?
A feature fusion approach combining artificial and deep learning features demonstrates satisfactory performance for detecting atrial fibrillation from short, noisy single-lead ECGs.
May aid noisy single-lead AF detection; leaves open prospective clinical validation before use.
Detecting atrial fibrillation (AF) of short single‐lead electrocardiogram (ECG) with low signal‐to‐noise ratio (SNR) is a key of the wearable heart monitoring system. This study proposed an AF detection method based on feature fusion to identify AF rhythm (A) from other three categories of ECG recordings, that is, normal rhythm (N), other rhythm (O), and noisy (∼) ECG recordings. So, the four categories, that is, N, A, O, and ∼ were identified from the database provided by PhysioNet/CinC Challenge 2017. The proposed method first unified the 9 to 60 seconds unbalanced ECG recordings into 30 s segments by copying, cutting, and symmetry. Then, 24 artificial features including waveform features, interval features, frequency‐domain features, and nonlinear feature were extracted relying on prior knowledge. Meanwhile, a 13‐layer one‐dimensional convolutional neural network (1‐D CNN) was constructed to yield 38 abstract features. Finally, 24 artificial features and 38 abstract features were fused to yield the feature matrix. Random forest was employed to classify the ECG recordings. In this study, the mean accuracy (Acc) of the four categories reached 0.857. The F1 of N, A, and O reached 0.837. The results exhibited the proposed method had relatively satisfactory performance for identifying AF from short single‐lead ECG recordings with low SNR.
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Bao et al. (2023) studied Atrial fibrillation. Feature fusion-based AF detection method using random forest was evaluated on Mean accuracy (Acc) of four categories and F1 score. A feature fusion-based detection method using artificial and abstract features achieved a mean accuracy of 0.857 and an F1 score of 0.837 for identifying atrial fibrillation from short single-lead ECGs.
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