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The automatic detection of atrial fibrillation based on electrocardiograph(ECG) signals has received wide attention both clinically and practically. Itis challenging to process ECG signals with cyclical pattern, varying length andunstable quality due to noise and distortion. Besides, there has beeninsufficient research on separating persistent atrial fibrillation fromparoxysmal atrial fibrillation, and little discussion on locating the onsetsand end points of AF episodes. It is even more arduous to perform well on thesetwo distinct but interrelated tasks, while avoiding the mistakes inherent fromstage-by-stage approaches. This paper proposes the Multi-level Multi-taskAttention-based Recurrent Neural Network for three-class discrimination onpatients and localization of the exact timing of AF episodes. Our modelcaptures three-level sequential features based on a hierarchical architectureutilizing Bidirectional Long and Short-Term Memory Network (Bi-LSTM) andattention layers, and accomplishes the two tasks simultaneously with amulti-head classifier. The model is designed as an end-to-end framework toenhance information interaction and reduce error accumulation. Finally, weconduct experiments on CPSC 2021 dataset and the result demonstrates thesuperior performance of our method, indicating the potential application ofMMA-RNN to wearable mobile devices for routine AF monitoring and earlydiagnosis.

MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation