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ECG Heartbeat Classification: A Deep Transferable Representation

arXiv 1805.00794 ↗First posted 19 Apr 2018cs.CY cs.LG stat.MLcited by 76

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Abstract (arXiv)

Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2018.

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Citing papers per year: 2021: 15; 2022: 13; 2023: 16; 2024: 14; 2025: 10; 2026: 8.

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  1. 2022Concept Activation Regions: A Generalized Framework For Concept-Based Explanationscited by 57
  2. 2022Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogramcited by 38
  3. 2024Time Series Diffusion in the Frequency Domaincited by 35
  4. 2023Explainable AI for Time Series via Virtual Inspection Layerscited by 23
  5. 2023EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Modelscited by 18
  6. 2021Universal Consistency of Deep Convolutional Neural Networkscited by 16
  7. 2022Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signalscited by 15
  8. 2022ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseasescited by 14
  9. 2024Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performancecited by 14
  10. 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariancecited by 13
  11. 2022Analysis of Augmentations for Contrastive ECG Representation Learningcited by 12
  12. 2021ECG Heartbeat Classification Using Multimodal Fusioncited by 10
38 more
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  2. 2025PainFormer: a Vision Foundation Model for Automatic Pain Assessmentcited by 10
  3. 2021Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputscited by 9
  4. 2023DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesiscited by 9
  5. 2024BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesiscited by 9
  6. 2021ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networkscited by 8
  7. 2025Tiny-BioMoE: a Lightweight Embedding Model for Biosignal Analysiscited by 8
  8. 2025Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generationcited by 8
  9. 2021Snippet Policy Network for Multi-class Varied-length ECG Early Classificationcited by 7
  10. 2023Scaling Representation Learning from Ubiquitous ECG with State-Space Modelscited by 7
  11. 2025MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fieldscited by 6
  12. 2021ECG Heart-beat Classification Using Multimodal Image Fusioncited by 5
  13. 2021A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classificationcited by 5
  14. 2024ECGformer: Leveraging transformer for ECG heartbeat arrhythmia classificationcited by 5
  15. 2021Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labelscited by 4
  16. 2022Leveraging Statistical Shape Priors in GAN-based ECG Synthesiscited by 4
  17. 2022Learning to Select Prototypical Parts for Interpretable Sequential Data Modelingcited by 4
  18. 2023Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancementcited by 4
  19. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4
  20. 2021Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrixcited by 3
  21. 2022ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT techniquecited by 3
  22. 2023Graph Neural Networks for Topological Feature Extraction in ECG Classificationcited by 3
  23. 2024LightFF: Lightweight Inference for Forward-Forward Algorithmcited by 3
  24. 2024Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Trainingcited by 3
  25. 2021HALF: Holistic Auto Machine Learning for FPGAscited by 2
  26. 2021Application of Adversarial Examples to Physical ECG Signalscited by 2
  27. 2021ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networkscited by 2
  28. 2023Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitalscited by 2
  29. 2024Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollerscited by 2
  30. 2022Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Datacited by 1
  31. 2022ECG beat classification using machine learning and pre-trained convolutional neural networkscited by 1
  32. 2023A Novel real-time arrhythmia detection model using YOLOv8cited by 1
  33. 2023ECGNet: A generative adversarial network (GAN) approach to the synthesis of 12-lead ECG signals from single lead inputscited by 1
  34. 2024TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answeringcited by 1
  35. 2024TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classificationcited by 1
  36. 2025Uncertainty-Aware Multi-view Arrhythmia Classification from ECGcited by 1
  37. 2025The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Taskscited by 1
  38. 2026EXCODER: EXplainable Classification Of DiscretE time series Representationscited by 1

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