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

2025-12-03

AI Summary

To address high model complexity and insufficient cross-modal feature fusion in multi-lead, long-sequence ECG analysis, this paper proposes EfficientECG—a lightweight end-to-end model. Built upon EfficientNet, it incorporates a cross-attention mechanism to enable dynamic and interpretable fusion of ECG time-series signals with structured patient attributes (e.g., age, sex). Extensive experiments on major benchmark datasets—including PTB-XL and CPSC2018—demonstrate that EfficientECG surpasses current state-of-the-art methods in classification accuracy, while reducing parameter count by 37% and accelerating inference speed by 2.1×. Moreover, it exhibits superior modeling capability for clinically relevant multimodal features. These advances establish an efficient and practical technical pathway for real-time, accurate ECG-assisted diagnosis in resource-constrained clinical settings.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Develops a lightweight deep learning model for accurate ECG classification
• Proposes cross-attention feature fusion to analyze multi-lead ECG with patient metadata
• Aims to reduce medical burden by automating diagnosis with high precision

Innovation

• Lightweight EfficientNet-based model for ECG classification
• Cross-attention feature fusion for multi-lead ECG data
• End-to-end training for automatic ECG feature extraction