# cpsc2020

**Repository Path**: deep-psp/cpsc2020

## Basic Information

- **Project Name**: cpsc2020
- **Description**: Searching for Premature Ventricular Contraction and Supraventricular Premature Beat from Long-term ECGs: The 3rd China Physiological Signal Challenge 2020
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2022-04-27
- **Last Updated**: 2024-03-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CPSC2020
Searching for Premature Ventricular Contraction and Supraventricular Premature Beat from Long-term ECGs: The 3rd China Physiological Signal Challenge 2020


## Update on 2020/12/31

Tests are done on a 60s segment (median-filtered and bandpassed, `sample1_fs250.mat` in [this folder](sample_data/)) of a subject with frequent PVC.
* r peak detections are done using [this function](https://github.com/DeepPSP/cpsc2020/blob/master/signal_processing/ecg_rpeaks_dl.py#L33).
* PVC beats are labeled using red vertical lines.
* missed PVC beats are labeled using yellow boxes.
![pred_ml](images/pred_ml.png)
![pred_dl_0.3](images/pred_dl_0.3_filtered.png)
![pred_dl_0.5](images/pred_dl_0.5_filtered.png)
* the first image is the result by a <strong>modified version</strong> of machine learning algorithms from [this repo](https://github.com/mondejar/ecg-classification) using rr features and wavelet features, with post-processing using clinical rules. Note that [phase_one_legacy](phase_one_legacy/) is one such modified version, which uses XGBoost instead of SVM and without clinical post-processing.
* the second image is the result of the sequence labeling deep learning model with probability threshold 0.3, and filtered by a deep learning classifier. The missed PVC beats are caused by this classifier.
* the last image is the result of the sequence labeling deep learning model with probability threshold 0.5, and filtered by a deep learning classifier.

### Observations
* an effective and robust rpeak (qrs complex) detector is crucial.
* the sequence labeling deep learning model (trained only for a dozen epochs because of the approaching deadline) tends to make false positive predictions but seldom has false negatives; while the deep learning classifier (trained only for several hundred epochs) has few false positives but has slightly higher probability to have false negatives.
* given a good rpeak detector, machine learning models might well be competitive against deep learning models.
* changing the threshold of the sequence labeling deep learning model from 0.5 to 0.3 can largely reduce the PVCerr score (punishment); further removing the post-filtering of the deep learning classifier might further reduce the scores, raising more false positives while reducing false negatives, considering that false negative has punishment 5 times as the punishment of false positives.

## Evaluation result
[Evaluation result](http://www.icbeb.org/CSPC2020) on the final full hidden test set

not fully listed

![final_eval_img](images/CPSC2020_final_result.png)


## References

[1] [ecg-classification](https://github.com/mondejar/ecg-classification)

[2] [BioSPPy](https://github.com/PIA-Group/BioSPPy)

[3] Cai, Wenjie, and Danqin Hu. "QRS complex detection using novel deep learning neural networks." IEEE Access (2020).

[4] [torch_ecg](https://github.com/DeepPSP/torch_ecg)


## TODO

[1] more robust qrs detector (finished)

[2] feature engineering (deprecated)

[3] deep learning model structure design (ongoing)

[4] use SNR to deal with (eliminate?) too noisy segments?

[5] etc....

# NOTE

[1] further updates will be done in [torch_ecg](https://github.com/DeepPSP/torch_ecg/tree/master/torch_ecg/train/train_crnn_cpsc2020), instead of this repository

[2] if you find [this function](https://github.com/DeepPSP/cpsc2020/blob/master/signal_processing/ecg_rpeaks_dl.py#L33) useful, please cite Reference [3]