LCCNN

CNN models for MIT-BIH Arrhythmia Database and Open-Source-Event-Driven-ECG-Dataset by PyTorch.

How to train

1. Download MIT-BIH Arrhythmia Database and Open-Source-Event-Driven-ECG-Dataset.
2. pip install -r requirements.txt
3. Rewrite setting_path.py.
4. Run preprocessing.py
5. Rewrite and Run 'train.py'

Models

• CNN
  • Simple CNN model for MIT-BIH Arrhythmia Database designed in [4].
• LCCNN
  • Simple CNN model for Open-Source-Event-Driven-ECG-Dataset designed in [4].
• LCCNNLight
  • CNN model for Open-Source-Event-Driven-ECG-Dataset lighter than LCCNN by using additional CNN layer.
• Resnet34
  • 1d-ResNet34 for MIT-BITH Arrhythmia Database
  • You can use this for Open-Source-Event-Driven-ECG-Dataset by small change.

Input Layers

• NormalCNN
  • Simple 2ch-CNN layer for Open-Source-Event-Driven-ECG-Dataset designed in [4].
• TimeEmbedding
  • Input layer for Open-Source-Event-Driven-ECG-Dataset that is 1ch-CNN layer + full-learnable positional encoding layer.
• TimeSin
  • Input layer for Open-Source-Event-Driven-ECG-Dataset that is 1ch-CNN layer + sinusoidal positional encoding layer.
• NonTimed
  • 1ch-CNN layer for Open-Source-Event-Driven-ECG-Dataset.

References

[1] https://physionet.org/content/mitdb/1.0.0/
[2] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209)
[3] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
[4] M. Saeed et al., "Evaluation of Level-Crossing ADCs for Event-Driven ECG Classification," in IEEE Transactions on Biomedical Circuits and Systems, vol. 15, no. 6, pp. 1129-1139, Dec. 2021, doi: 10.1109/TBCAS.2021.3136206.