A Personalized Zero-Shot ECG Arrhythmia Monitoring System
Results
| Method | Accuracy | Specificity | Precision | Recall | F1-Score |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Kiranyaz *et al.*  | 0.959 | 0.971 | 0.842 | 0.888 | 0.864 |
| Zhai *et al.*  | 0.968 | 0.976 | 0.879 | 0.920 | 0.899 |
| Li *et al.* | 0.920 | 0.918 | 0.628 | 0.933 | 0.751 |
| Zhou *et al.*  | 0.979 | 0.989 | 0.908 | 0.897 | 0.902 |
| SAE-based  | 0.947 | 0.968 | 0.779 | 0.794 | 0.786 |
| NPE-based (ours)  | 0.947 | 0.968 | 0.779 | 0.794 | 0.786 |
| ABS  | 0.977 | **0.995** | **0.956** | 0.825 | 0.886 |
| Domain Adaptation (ours)  | 0.978 | 0.987 | 0.911 | 0.907 | 0.909 |
| Ensemble (ours)  | **0.982** | 0.988 | 0.919 | **0.937** | **0.928** |
 Personalized classifiers.
 Zero-shot classifiers.
 Zero-shot classifiers.
Setup
Python (3.8.10) dependencies:
- matplotlib (3.4.2), numpy (1.19.5), scipy (1.6.3), pandas (1.2.4), seaborn (0.11.1, optional)
- torch (1.10.2+cu113)
- wfdb (3.3.0)
- import_ipynb (0.1.3)
To minimize conflict, our versions are given as reference.
Replicating Our Results
- Download the MIT-BIH Arrhythmia Database.
- Run ecg_beat_extraction.ipynb twice to generate single beats and beat-trios.
- There should be 6 files generated.
- Run ecg_dataset_preparation.ipynb twice to generate datasets from single beats and beat-trios.
- This generates dictionaries for each user. Save the dictionaries for single beats (and optionally for beat-trios).
- We perform domain adaptation at this stage using the generated dictionaries for each user.
- There should be 68 dataset files generated, and dictionaries for each user.
- To train your own classifier, go to train.ipynb. Otherwise, skip this part.
- To test with pretrained weights go to pretrained_*.ipynb files.