ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks

TL;DR

This paper introduces ABCNN, a novel deep learning model combining CNN and multi-head attention, capable of directly analyzing raw ECG signals for accurate heart arrhythmia detection, outperforming existing methods.

Contribution

The paper presents a new attention-based CNN model that automatically extracts features from raw ECG data, reducing preprocessing effort and improving classification accuracy.

Findings

ABCNN outperforms baseline models in arrhythmia detection

The model effectively recognizes five types of arrhythmias

Visualization confirms meaningful feature extraction

Abstract

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.