ArticleiScience2021
Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 180 citations in OpenAlex.
- Clinically-aligned explainable AI for atrial fibrillation detection: A U-Net inspired multi-lead ECG analysis framework.Computer methods and programs in biomedicine · 2026Article
- [Multi-lead electrocardiogram atrial fibrillation detection algorithm based on multi-scale patch-based attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Real-Time Integration of an AI-Based ECG Interpretation System in the Emergency Department: A Pragmatic Alternating-Day Study of Diagnostic Performance and Clinical Process Metrics.Healthcare (Basel, Switzerland) · 2026Article
- Explainable artificial intelligence in electrocardiography: A systematic review.Biomedical signal processing and control · 2026Article
- Long Short-Term Memory-GPT-4 Integration for Interpretable Biomedical Signal Classification: Proof-of-Concept Study.JMIR formative research · 2026Article
- Real-time ECG-based detection of cardiovascular diseases using balanced and interpretable machine learning approaches.Physical and engineering sciences in medicine · 2026Article
- [Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects.Scientific reports · 2025Article
- [ResLSTM-TemporalSE: an automated classification model for multi-lead ECG signals].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Assessing the Impact of Downsampled ECGs and Alternative Loss Functions in Multi-Label Classification of 12-Lead ECGs.Cardiovascular engineering and technology · 2025Article
- Lightweight Deep Learning Architecture for Multi-Lead ECG Arrhythmia Detection.Sensors (Basel, Switzerland) · 2025Article
- [A myocardial infarction detection and localization model based on multi-scale field residual blocks fusion with modified channel attention].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Enhancing Heart Disease Diagnosis Using ECG Signal Reconstruction and Deep Transfer Learning Classification with Optional SVM Integration.Diagnostics (Basel, Switzerland) · 2025Article
- Atrial fibrillation detection via contactless radio monitoring and knowledge transfer.Nature communications · 2025Article
- Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.The Korean journal of internal medicine · 2025Article
- Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques.Sensors (Basel, Switzerland) · 2025Review
- Clinically interpretable multiclass neural network for discriminating cardiac diseases.Heliyon · 2025Article
- Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.European heart journal. Digital health · 2025Article
- Correlating Electrocardiograms with Echocardiographic Parameters in Hemodynamically-Significant Aortic Regurgitation Using Deep Learning.Acta Cardiologica Sinica · 2024Article
- Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become a hot research topic. In this paper, we developed a deep neural network for automatic classification of cardiac arrhythmias from 12-lead ECG recordings. Experiments on a public 12-lead ECG dataset showed the effectiveness of our method. The proposed model achieved an average F1 score of 0.813. The deep model showed superior performance than 4 machine learning methods learned from extracted expert features. Besides, the deep models trained on single-lead ECGs produce lower performance than using all 12 leads simultaneously. The best-performing leads are lead I, aVR, and V5 among 12 leads. Finally, we employed the SHapley Additive exPlanations method to interpret the model's behavior at both the patient level and population level.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.