ArticleScientific reports2024
xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- AI-Assisted Multilabel Diagnosis of 12-Lead Electrocardiograms Using an Interpretable Stacked Deep Learning Model with External Validation.Diagnostics (Basel, Switzerland) · 2026Article
- Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations.Scientific reports · 2026Article
- Article
- Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification.European heart journal. Digital health · 2026Article
- xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification.NPJ digital medicine · 2026Article
- Interpretable multimodal zero shot ECG diagnosis via structured clinical knowledge alignment.NPJ cardiovascular health · 2026Article
- Research on cross-dataset cardiac signal domain generalization and feature interpretability.Scientific reports · 2025Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Patient-Centered Risk Prediction, Prevention, and Intervention Platform (TIMELY) to Support the Continuum of Care in Coronary Artery Disease Using eHealth and Artificial Intelligence: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Article
- ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.Proceedings of machine learning research · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.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
- Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features.Diagnostics (Basel, Switzerland) · 2025Article
- Detection of Atrial Fibrillation in Holter ECG Recordings by ECHOView Images: A Deep Transfer Learning Study.Diagnostics (Basel, Switzerland) · 2025Article
- High-accuracy ECG image interpretation using parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning with multimodal LLaMA V.3.2.BMJ digital health & AI · 2025Article
- Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI.Computational and structural biotechnology journal · 2025Article
- xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases (
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Registered trials
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 Socratic graph.