Search

Atrial fibrillation (AF) is a common arrhythmia, and its incidence increases with age. Many methods have been developed to identify AF, including both the hand-picked features by experts and the recent emerging artificial intelligent (AI) methods. As the traditional hand-picked features have almost reached the boundary of their capability, the AI methods have shown their great potentials to achieve high accuracy for the AF identification. However, some common AI methods, especially deep learning methods, do not provide good properties of interpretability, making it difficult to explore the internal relationship between input and prediction results. In addition, most of the reported methods are only for the intra-patient test of AF and Non-AF. In this study, we try to develop an AF detector based on a twin-attentional convolutional long short-term memory neural network (TAC-LSTM), which can not only generate results with high accuracy but also enable a human-friendly function to provide the possible explanations of the automated extracted features by AI. TAC-LSTM was applied to extract multi-domain features of ECG signals for AF detection and to mine the influence of different input segments on the final prediction results. Finally, the proposed method is validated on the MIT-BIH Atrial Fibrillation Database (AFDB) with intra-patient test and inter-patient test and the results also have shown that multi-domain features extracted by TAC-LSTM can provide more useful information. Collectively, TAC-LSTM can be used for clinicians as an auxiliary diagnostic tool. • A novel deep neural network with multi-domain input layers is proposed. • CNN and LSTM are employed to extract short-term and long-term dependence features. • In AF detection, multi-domain information is characterized by complementary. • Fusing signal time and time-frequency domain information improves model performance. • Our method mines the relationship between the input segment and prediction results. [ABSTRACT FROM AUTHOR]