  Electrocardiography (ECG) signal is a highly applied measurement for
individual heart condition, and much effort have been endeavored towards
automatic heart arrhythmia diagnosis based on machine learning. However,
traditional machine learning models require large investment of time and effort
for raw data preprocessing and feature extraction, as well as challenged by
poor classification performance. Here, we propose a novel deep learning model,
named Attention-Based Convolutional Neural Networks (ABCNN) that taking
advantage of CNN and multi-head attention, to directly work on the raw ECG
signals and automatically extract the informative dependencies for accurate
arrhythmia detection. To evaluate the proposed approach, we conduct extensive
experiments over a benchmark ECG dataset. Our main task is to find the
arrhythmia from normal heartbeats and, at the meantime, accurately recognize
the heart diseases from five arrhythmia types. We also provide convergence
analysis of ABCNN and intuitively show the meaningfulness of extracted
representation through visualization. The experimental results show that the
proposed ABCNN outperforms the widely used baselines, which puts one step
closer to intelligent heart disease diagnosis system.
