mlsanityData to 7 Oct 2026

LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

arXiv 2207.12381 ↗First posted 25 Jul 2022cs.CV cs.AIcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 18 of its 77 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

18 of 77 references.

References · 18

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6 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 2; 2025: 4; 2026: 1.

11 papers.

  1. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  2. 2022Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networkscited by 2
  3. 2025A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibilitycited by 2
  4. 2022A novel deep learning-based approach for sleep apnea detection using single-lead ECG signalscited by 1
  5. 2025ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classificationcited by 1
  6. 2022Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integrationcited by 0
  7. 2023MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classificationcited by 0
  8. 2024ECG Arrhythmia Detection Using Disease-specific Attention-based Deep Learning Modelcited by 0
  9. 2025EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classificationcited by 0
  10. 2025Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signalscited by 0
  11. 2026Information-theoretic Multimodal Representation Learning for Electrocardiogram Signalscited by 0