ArticleCirculation. Arrhythmia and electrophysiology2021
Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers.
Article in Circulation. Arrhythmia and electrophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 52 citations in OpenAlex.
- Machine Learning-based Early Detection of Intraoperative Anaphylaxis Among Patients with Hypotension Using Real-World Physiological Time Series Data.Journal of medical systems · 2026Article
- Machine Learning in Nonischemic Cardiomyopathy: Phenotyping, Mechanism Discovery, and Clinical Applications.Cardiology in review · 2026Article
- Explainable artificial intelligence in electrocardiography: A systematic review.Biomedical signal processing and control · 2026Article
- Explainable deep learning-based classification of Wolff-Parkinson-White electrocardiographic signals.Frontiers in physiology · 2026Article
- Clinical care of family members of patients with dilated cardiomyopathy.European heart journal · 2025Article
- Lightweight Deep Learning Models with Explainable AI for Early Alzheimer's Detection from Standard MRI Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Assessing the Impact of Downsampled ECGs and Alternative Loss Functions in Multi-Label Classification of 12-Lead ECGs.Cardiovascular engineering and technology · 2025Article
- Enhancing explainability in clinical deep-learning models: Latent-space variable decoding is superior to gradient-weighted class activation mapping.Heart rhythm O2 · 2025Article
- Different underlying aetiologies in patients presenting with ventricular tachycardia fulfilling task force criteria for arrhythmogenic right ventricular cardiomyopathy: initial suspicion based on the 12-lead electrocardiogram.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Review
- JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia.Journal of arrhythmia · 2024Article
- Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.European heart journal. Digital health · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Article
- Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.JACC. Advances · 2023Article
- Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers.The international journal of cardiovascular imaging · 2023Article
- Should we offer preventive treatment to all carriers of PLN p.(Arg14del) variant? : Con: Do no harm to asymptomatic carriers.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2023Review
- Variational auto-encoders improve explainability over currently employed heatmap methods for deep learning-based interpretation of the electrocardiogram.European heart journal. Digital health · 2022Article
- Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022Article
- Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encodersEuropean heart journal. Digital health · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of PLN (phospholamban) p.Arg14del mutation carriers, we aimed to investigate whether a novel DNN-based approach can identify established ECG features, but moreover, we aimed to expand our knowledge on novel ECG features in these patients.
methodsA DNN was developed on 12-lead median beat ECGs of 69 patients and 1380 matched controls and independently evaluated on 17 patients and 340 controls. Differentiating features were visualized using Guided Gradient Class Activation Mapping++. Novel ECG features were tested for their diagnostic value by adding them to a logistic regression model including established ECG features.
resultsThe DNN showed excellent discriminatory performance with a c-statistic of 0.95 (95% CI, 0.91-0.99) and sensitivity and specificity of 0.82 and 0.93, respectively. Visualizations revealed established ECG features (low QRS voltages and T-wave inversions), specified these features (eg, R- and T-wave attenuation in V2/V3) and identified novel PLN-specific ECG features (eg, increased PR-duration). The logistic regression baseline model improved significantly when augmented with the identified features (
conclusionsA DNN-based feature detection approach was able to discover and visualize disease-specific ECG features in PLN mutation carriers and revealed yet unidentified features. This novel approach may help advance diagnostic capabilities in daily practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.