A Novel Interpretable Method Based on Dual-Level Attentional Deep Neural Network for Actual Multilabel Arrhythmia Detection

Abstract

Arrhythmia accounts for more than 80% of sudden cardiac death, and its incidence rate has increased rapidly recently. Nowadays, many studies have applied artificial intelligence (AI) methods to arrhythmia detection. Deep learning approaches can improve model performance but lack the capability to mine the relationship between the electrocardiogram (ECG) samples and final results. Furthermore, most arrhythmia diagnosis methods aim at only multiclassification problems. However, in many cases, an ECG recording may have more than one arrhythmia disease. In this article, we develop an accurate and interpretable model for multilabel ECG signals, which is called dual-level attentional convolutional long short-term memory neural network (DLA-CLSTM). Convolutional layers, bidirectional long short-term memory layers, and attention mechanisms are used for extracting beat- and rhythm-level information. Notably, the attention mechanism helps the model be more interpretable. In addition, we design a weighted multilabel classifier to address multilabel and imbalance problems. Finally, the MIT-BIH Arrhythmia Database and the 1st China Physiological Signal Challenge dataset are utilized to evaluate DLA-CLSTM. Compared with existing methods, DLA-CLSTM can improve the accuracy by 22.50% and the F1-macro by 20.51% on average. Further analysis shows that the areas focused by DLA-CLSTM match the clinical judgment basis to some extent. Collectively, DLA-CLSTM has good potential for assisting clinicians in real applications.