Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG

TL;DR

DenseECG is an end-to-end deep learning model that effectively detects atrial fibrillation from ECG data, outperforming existing models without complex pre-processing or feature engineering.

Contribution

The paper introduces DenseECG, a 5-layer densely connected CNN that simplifies AF detection by eliminating the need for complicated data pre-processing.

Findings

Outperforms state-of-the-art AF detection models on PhysioNet dataset

Achieves high accuracy with minimal data pre-processing

Demonstrates effectiveness of end-to-end deep learning for ECG analysis

Abstract

Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large number of people around the world. If left undetected, it will develop into chronic disability or even early mortality. However, patients who have this problem can barely feel its presence, especially in its early stage. A non-invasive, automatic, and effective detection method is therefore needed to help early detection so that medical intervention can be implemented in time to prevent its progression. Electrocardiogram (ECG), which records the electrical activities of the heart, has been widely used for detecting the presence of AF. However, due to the subtle patterns of AF, the performance of detection models have largely depended on complicated data pre-processing and expertly engineered features. In our work, we developed DenseECG, an end-to-end model based on 5 layers 1D densely connected convolutional neural network. We trained our model using the publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC) Challenge containing 8528 single-lead ECG recordings of short-term heart rhythms (9-61s). Our trained model was able to outperform the other state-of-the-art AF detection models on this dataset without complicated data pre-processing and expert-supervised feature engineering.