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Classification of 12-Lead ECG Signals with Bi-directional LSTM Network

arXiv 1811.02090 ↗First posted 5 Nov 2018cs.CVcited by 8

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Abstract (arXiv)

We propose a recurrent neural network classifier to detect pathologies in 12-lead ECG signals and train and validate the classifier with the Chinese physiological signal challenge dataset (http://www.icbeb.org/Challenge.html). The recurrent neural network consists of two bi-directional LSTM layers and can train on arbitrary-length ECG signals. Our best trained model achieved an average F1 score of 74.15% on the validation set. Keywords: ECG classification, Deep learning, RNN, Bi-directional LSTM, QRS detection.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2018.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 1; 2025: 1.

8 papers.

  1. 2023Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancementcited by 4
  2. 2023MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classificationcited by 4
  3. 2023MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosiscited by 3
  4. 2025Comparative Analysis of Data Augmentation for Clinical ECG Classification with STARcited by 3
  5. 2021CRT-Net: A Generalized and Scalable Framework for the Computer-Aided Diagnosis of Electrocardiogram Signalscited by 2
  6. 2021SIM-ECG: A Signal Importance Mask-driven ECGClassification Systemcited by 0
  7. 2022A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performancecited by 0
  8. 2024Revisiting the Disequilibrium Issues in Tackling Heart Disease Classification Taskscited by 0