mlsanityData to 7 Oct 2026

Automatic diagnosis of the 12-lead ECG using a deep neural network

arXiv 1904.01949 ↗First posted 2 Apr 2019cs.LG eess.SP stat.MLcited by 164

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Abstract (arXiv)

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

Key facts. Cited by 164 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2019.

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Citing papers per year: 2021: 7; 2022: 16; 2023: 31; 2024: 23; 2025: 41; 2026: 46.

164 papers.

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  1. 2024Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogramcited by 84
  2. 2021Self-supervised representation learning from 12-lead ECG datacited by 80
  3. 2023Effectively Modeling Time Series with Simple Discrete State Spacescited by 54
  4. 2024An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domainscited by 27
  5. 2024Teach Multimodal LLMs to Comprehend Electrocardiographic Imagescited by 25
  6. 2021Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modelingcited by 24
  7. 2024Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecturecited by 17
  8. 2024Boosting Masked ECG-Text Auto-Encoders as Discriminative Learnerscited by 16
  9. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  10. 2023Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discoverycited by 15
  11. 2023Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadatacited by 15
  12. 2021Addressing catastrophic forgetting for medical domain expansioncited by 12
38 more
  1. 2022Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteriacited by 11
  2. 2022Improving ECG Classification Interpretability using Saliency Mapscited by 9
  3. 2022Advancing the State-of-the-Art for ECG Analysis through Structured State Space Modelscited by 9
  4. 2024Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECGcited by 9
  5. 2025Uncertainty-Aware Large Language Models for Explainable Disease Diagnosiscited by 9
  6. 2025BenchECG and xECG: a benchmark and baseline for ECG foundation modelscited by 8
  7. 2021Robustness of convolutional neural networks to physiological ECG noisecited by 7
  8. 2023Deep learning based ECG segmentation for delineation of diverse arrhythmiascited by 7
  9. 2023In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detectioncited by 7
  10. 2023Masked Transformer for Electrocardiogram Classificationcited by 7
  11. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7
  12. 2025CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Modelscited by 7
  13. 2022Class-Specific Explainability for Deep Time Series Classifierscited by 6
  14. 2023Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Studycited by 6
  15. 2023Sparse learned kernels for interpretable and efficient medical time series processingcited by 6
  16. 2023RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECGcited by 6
  17. 2025Benchmarking ECG FMs: A Reality Check Across Clinical Taskscited by 6
  18. 2023MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferringcited by 5
  19. 2024MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Networkcited by 5
  20. 2024Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Featurescited by 5
  21. 2024AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settingscited by 5
  22. 2025Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputscited by 5
  23. 2022ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasetscited by 4
  24. 2023Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classificationcited by 4
  25. 2023Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detectioncited by 4
  26. 2024Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learningcited by 4
  27. 2025NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Modelscited by 4
  28. 2022Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Networkcited by 3
  29. 2022On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECGcited by 3
  30. 2022Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learningcited by 3
  31. 2023MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosiscited by 3
  32. 2023TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcarecited by 3
  33. 2024Transfer Learning in ECG Diagnosis: Is It Effective?cited by 3
  34. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  35. 2025Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for ECG Foundation Modelscited by 3
  36. 2025Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Researchcited by 3
  37. 2025Interpretable Multimodal Zero-Shot ECG Diagnosis via Structured Clinical Knowledge Alignmentcited by 3
  38. 2021Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Thingscited by 2

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