MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation

Abstract

Background:Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization. Methodology:We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis. Results:We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks. Conclusions:Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention.