Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Atrial fibrillation detection,Convolutional long short-term memory neural networks,ECG,Interpretable attention mechanism,Wavelet transform
论文评审过程：Received 23 July 2019, Revised 4 November 2019, Accepted 28 December 2019, Available online 2 January 2020, Version of Record 7 March 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.105460
