ArticleScientific reports2021
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.Journal of medical systems · 2026Article
- MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.Scientific reports · 2026Article
- ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction.IEEE transactions on bio-medical engineering · 2025Article
- Deep Learning-Based Risk Assessment and Prediction of Cardiac Outcomes Using Single-Lead 24-Hour Holter-ECG in Patients with Heart Failure or Myocardial Infarction.Journal of clinical medicine · 2025Article
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- Assessing the Impact of Downsampled ECGs and Alternative Loss Functions in Multi-Label Classification of 12-Lead ECGs.Cardiovascular engineering and technology · 2025Article
- Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.Biomedical engineering letters · 2025Review
- Deep learning on electrocardiogram waveforms to stratify risk of obstructive stable coronary artery disease.European heart journal. Digital health · 2025Article
- Evaluating the feasibility of 12-lead electrocardiogram reconstruction from limited leads using deep learning.Communications medicine · 2025Article
- A novel approach to graph distinction through GENEOs and permutants.Scientific reports · 2025Article
- Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation.Journal of personalized medicine · 2024Article
- Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.European heart journal. Digital health · 2024Article
- [Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering.Diagnostics (Basel, Switzerland) · 2023Article
- Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Interpreting models interpreting brain dynamics.Scientific reports · 2022Article
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
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