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Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

arXiv 1707.01836 ↗First posted 6 Jul 2017cs.CVcited by 52

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Abstract (arXiv)

We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2017.

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Citing papers per year: 2021: 10; 2022: 7; 2023: 7; 2024: 3; 2025: 12; 2026: 13.

52 papers.

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  1. 2021Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modelingcited by 24
  2. 2023ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learningcited by 10
  3. 2024ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modelingcited by 9
  4. 2021ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networkscited by 8
  5. 2025Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generationcited by 8
  6. 2021XAI Methods for Neural Time Series Classification: A Brief Reviewcited by 7
  7. 2025Explainable Deep Neural Network for Multimodal ECG Signals: Intermediate vs Late Fusioncited by 6
  8. 2021A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classificationcited by 5
  9. 2023SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classificationcited by 5
  10. 2023MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classificationcited by 4
  11. 2021ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networkscited by 3
  12. 2022Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learningcited by 3
38 more
  1. 2021HALF: Holistic Auto Machine Learning for FPGAscited by 2
  2. 2021Application of Adversarial Examples to Physical ECG Signalscited by 2
  3. 2023Optimizing Neural Network Scale for ECG Classificationcited by 2
  4. 2024Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extractioncited by 2
  5. 2025Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Frameworkcited by 2
  6. 2021ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Modelscited by 1
  7. 2022ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methodscited by 1
  8. 2023Statistical Tests for Replacing Human Decision Makers with Algorithmscited by 1
  9. 2023HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiogramscited by 1
  10. 2025Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Modelscited by 1
  11. 2025A Comprehensive Benchmark for Electrocardiogram Time-Seriescited by 1
  12. 2025CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECGcited by 1
  13. 2026ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Modelcited by 1
  14. 2026ECG-Lens: Benchmarking ML & DL Models on PTB-XL Datasetcited by 1
  15. 2021Neurological Status Classification Using Convolutional Neural Networkcited by 0
  16. 2021Dynamic-Deep: Tune ECG Task Performance and Optimize Compression in IoT Architecturescited by 0
  17. 2022Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Networkcited by 0
  18. 2022A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performancecited by 0
  19. 2022Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretationcited by 0
  20. 2022Evaluating Feature Attribution Methods for Electrocardiogramcited by 0
  21. 2022Using machine learning algorithms to determine the post-COVID state of a person by his rhythmogramcited by 0
  22. 2023Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classificationcited by 0
  23. 2024High quality ECG dataset based on MIT-BIH recordings for improved heartbeats classificationcited by 0
  24. 2025A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysiscited by 0
  25. 2025Interpretable AI for Time-Series: Multi-Model Heatmap Fusion with Global Attention and NLP-Generated Explanationscited by 0
  26. 2025NeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignmentcited by 0
  27. 2025EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysiscited by 0
  28. 2025MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Featurescited by 0
  29. 2025UniCoMTE: A Universal Counterfactual Framework for Explaining Time-Series Classifiers on ECG Datacited by 0
  30. 2026ELF: A Family of Encoder-Free ECG-Language Modelscited by 0
  31. 2026Electrocardiogram Classification with Transformers Using Koopman and Wavelet Featurescited by 0
  32. 2026Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyMLcited by 0
  33. 2026EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillationcited by 0
  34. 2026ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systemscited by 0
  35. 2026Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classificationcited by 0
  36. 2026Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Studycited by 0
  37. 2026Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMscited by 0
  38. 2026HealthCAT: An Interpretable Encoder-only Transformer Framework for Health Indicator Prediction and Temporal Interpretation of Wearable Sensor Datacited by 0

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