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

arXiv: https://arxiv.org/abs/1904.01949 · HTML: https://mlsanity.com/paper/1904.01949/

## Key facts

- Cited by 164 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 164)

- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Effectively Modeling Time Series with Simple Discrete State Spaces](https://mlsanity.com/paper/2303.09489/) (2023)
- [Teach Multimodal LLMs to Comprehend Electrocardiographic Images](https://mlsanity.com/paper/2410.19008/) (2024)
- [An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains](https://mlsanity.com/paper/2410.04133/) (2024)
- [Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling](https://mlsanity.com/paper/2104.04569/) (2021)
- [Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2410.08559/) (2024)
- [Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners](https://mlsanity.com/paper/2410.02131/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata](https://mlsanity.com/paper/2308.15291/) (2023)
- [Addressing catastrophic forgetting for medical domain expansion](https://mlsanity.com/paper/2103.13511/) (2021)
- [Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria](https://mlsanity.com/paper/2211.01738/) (2022)
- [Improving ECG Classification Interpretability using Saliency Maps](https://mlsanity.com/paper/2201.04070/) (2022)
- [Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models](https://mlsanity.com/paper/2211.07579/) (2022)
- [Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG](https://mlsanity.com/paper/2407.11481/) (2024)
- [Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis](https://mlsanity.com/paper/2505.03467/) (2025)
- [BenchECG and xECG: a benchmark and baseline for ECG foundation models](https://mlsanity.com/paper/2509.10151/) (2025)
- [Robustness of convolutional neural networks to physiological ECG noise](https://mlsanity.com/paper/2108.01995/) (2021)
- [Deep learning based ECG segmentation for delineation of diverse arrhythmias](https://mlsanity.com/paper/2304.06237/) (2023)
- [In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection](https://mlsanity.com/paper/2304.06427/) (2023)
- [Masked Transformer for Electrocardiogram Classification](https://mlsanity.com/paper/2309.07136/) (2023)
- [Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives](https://mlsanity.com/paper/2506.24124/) (2025)
- [Benchmarking ECG FMs: A Reality Check Across Clinical Tasks](https://mlsanity.com/paper/2509.25095/) (2025)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [Class-Specific Explainability for Deep Time Series Classifiers](https://mlsanity.com/paper/2210.05411/) (2022)
- [Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study](https://mlsanity.com/paper/2306.10494/) (2023)
- [Sparse learned kernels for interpretable and efficient medical time series processing](https://mlsanity.com/paper/2307.05385/) (2023)
- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models](https://mlsanity.com/paper/2510.13068/) (2025)

## 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.