EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification

TL;DR

EfficientECG is a lightweight deep learning model that uses cross-attention and feature fusion to improve ECG classification accuracy, handling multi-lead data efficiently for better cardiac diagnosis.

Contribution

The paper introduces EfficientECG, a novel lightweight model based on EfficientNet, with a cross-attention feature fusion mechanism for multi-lead ECG analysis.

Findings

Outperforms state-of-the-art models in accuracy and efficiency

Effectively handles high-frequency, long-sequence ECG data

Successfully integrates multi-feature data such as gender and age

Abstract

Electrocardiogram is a useful diagnostic signal that can detect cardiac abnormalities by measuring the electrical activity generated by the heart. Due to its rapid, non-invasive, and richly informative characteristics, ECG has many emerging applications. In this paper, we study novel deep learning technologies to effectively manage and analyse ECG data, with the aim of building a diagnostic model, accurately and quickly, that can substantially reduce the burden on medical workers. Unlike the existing ECG models that exhibit a high misdiagnosis rate, our deep learning approaches can automatically extract the features of ECG data through end-to-end training. Specifically, we first devise EfficientECG, an accurate and lightweight classification model for ECG analysis based on the existing EfficientNet model, which can effectively handle high-frequency long-sequence ECG data with various leading types. On top of that, we next propose a cross-attention-based feature fusion model of EfficientECG for analysing multi-lead ECG data with multiple features (e.g., gender and age). Our evaluations on representative ECG datasets validate the superiority of our model against state-of-the-art works in terms of high precision, multi-feature fusion, and lightweights.