Early detection of cardiovascular events using minimal ECG leads
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Publisher
AIP Publishing
Link
http://aip.scitation.org/doi/pdf/10.1063/5.0178889
Reference18 articles.
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3. A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge distillation
4. Linschmann, O., Rohr, M., Leonhardt, K. S., & Antink, C. H. (2021, September). Multi-label classification of cardiac abnormalities for multi-lead ECG recordings based on auto-encoder features and a neural network classifier. In 2021 Computing in Cardiology (CinC) (Vol. 48, pp. 1–4). IEEE.
5. Pathinarupothi, R. K., Vinaykumar, R., Rangan, E., Gopalakrishnan, E., & Soman, K. P. (2017, February). Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 293–296). IEEE.
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