Key result
HADLN achieves ~87% accuracy for automated arrhythmia classification.
Why the study?
Traditional deep convolutional neural network models for ECG analysis have limitations including ignoring context correlation and gradient dispersion, motivating development of improved arrhythmia classification methods.
A novel hybrid attention-based deep learning network (HADLN) demonstrates high accuracy and F1-score for automated classification of atrial fibrillation and other rhythms from ECG signals.
May support automated ECG arrhythmia detection; hypothesis-generating and requires prospective clinical validation.
In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long-short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.
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Jiang et al. (2021) studied Arrhythmia (Atrial Fibrillation) (n=8,528). Hybrid Attention-Based Deep Learning Network (HADLN) vs. Other deep learning models (ResNet, CL3, QRS-LSTM, Dense-net) was evaluated on Classification accuracy. The Hybrid Attention-Based Deep Learning Network (HADLN) achieved an accuracy of 0.867 and an F1-score of 0.880 for automated arrhythmia classification on 10-fold cross-validation.
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