ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks
This work addresses the problem of reducing manual preprocessing and improving accuracy in ECG-based arrhythmia detection for medical diagnostics, representing an incremental advance in deep learning applications.
The authors tackled automatic heart arrhythmia diagnosis from ECG signals by proposing an attentional convolutional neural network (ABCNN) that directly processes raw data, achieving improved classification performance over baselines on a benchmark dataset.
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.