EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classification
This work addresses the need for accurate and fast ECG diagnostic models to reduce the burden on medical workers, though it appears incremental by building on existing EfficientNet and adding feature fusion.
The paper tackled the problem of high misdiagnosis rates in ECG classification by proposing EfficientECG, a lightweight deep learning model based on EfficientNet, and a cross-attention-based feature fusion model for multi-lead data, achieving high precision and efficiency in evaluations on representative datasets.
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.