![[2207.12381] LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2207.12381] LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification
Summary
The paper presents LightX3ECG, a lightweight and explainable deep learning system designed for classifying cardiovascular abnormalities using only three ECG leads, enhancing accessibility and convenience in monitoring heart health.
Why It Matters
With cardiovascular diseases being a leading health threat, this research addresses the need for early detection methods. By utilizing a simplified 3-lead ECG system, it offers a practical solution for widespread monitoring, potentially improving patient outcomes and making heart health assessments more accessible.
Key Takeaways
- LightX3ECG utilizes only three leads for ECG classification, making it more accessible.
- The system is designed to be lightweight and explainable, enhancing usability in clinical settings.
- Early detection of cardiovascular abnormalities can significantly improve treatment outcomes.
Computer Science > Computer Vision and Pattern Recognition arXiv:2207.12381 (cs) [Submitted on 25 Jul 2022 (v1), last revised 16 Feb 2026 (this version, v2)] Title:LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification Authors:Khiem H. Le, Hieu H. Pham, Thao BT. Nguyen, Tu A. Nguyen, Tien N. Thanh, Cuong D. Do View a PDF of the paper titled LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification, by Khiem H. Le and 5 other authors View PDF HTML (experimental) Abstract:Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the current research, standard 12-lead ECG is mainly used. However, using a lower number of leads can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices. In this research, we develop a novel deep learning system to accurately identify multiple cardiovascular abnormalities by using only three ECG leads. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2207.12381 [cs.CV] (or arXiv:2207.12381v2 [cs.C...
