This repository is accompanying our article Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL, which builds on the PTB-XL dataset. It allows to reproduce the ECG benchmarking experiments described in the paper and to benchmark user-provided models within our framework. We also maintain a leaderboard for the described PTB-XL dataset on this page, so feel free to submit your results as PRs.
Please acknowledge our work by citing the corresponding articles listed in References below.
Install the dependencies by creating a conda environment:
conda env create -f ecg_env.yml
conda activate ecg_python37
Download and prepare the datasets (PTB-XL and ICBEB) via the follwing bash-script:
./get_datasets.sh
This script first downloads PTB-XL from PhysioNet and stores it in data/ptbxl/.
Afterwards all training data from the ICBEB challenge 2018 is downloaded and temporally stored in tmp_data/.
After downloading and unzipping code/utils/convert_ICBEB.py is called which stores the data in appropriate format in data/ICBEB/.
Change directory: cd code and then call
python reproduce_results.py
This will perform all experiments for all models used in the paper.
Depending on the executing environment, this will take up to several hours.
Once finished, all trained models, predictions and results are stored in output/,
where for each experiment a sub-folder is created each with data/, models/ and results/ sub-sub-folders.
We also provide a compressed zip-archive containing the output folder corresponding to our runs including trained models and predictions from our runs mentioned in the leaderboard below.
For creating custom benchmarking results our recommendation is as follows:
- create your model
code/models/your_model.pywhich implements a standard classifier interface withfit(X_train, y_train, X_val, y_val)andpredict(X) - create a config file
code/configs/your_configs.pywith name, type and parameters (if needed) - add your modeltype and model import to the cases in
perform-function ofcode/experiments/scp_experiment.py(already added for demonstration purpose!) - add your model-config to
modelsand perform your experiment as below (adjusted code ofcode/reproduce_results.py):
from experiments.scp_experiment import SCP_Experiment
from configs.your_custom_configs import your_custom_config
datafolder = '../data/ptbxl/'
outputfolder = '../output/'
models = [your_custom_config]
e = SCP_Experiment('your_custom_experiment', 'diagnostic', datafolder, outputfolder, models)
e.prepare()
e.perform()
e.evaluate()Altough we recommend to use our framework, custom evaluation of custom models is still possible via calling code.utils.utils.evaluate_experiment(y_true, y_pred, thresholds)
manually with classwise thresholds.
For e.evaluate(): If the name of the experiment is exp_ICBEB classifier thresholds are needed.
In any other case evaluate_experiment(y_true, y_pred) will return a dictionary with macro_auc and Fmax (both metrics are without any explicitly needed thresholds).
In case of exp_ICBEB we offer two functions for computing thresholds (located in code/utils/utils.py):
thresholds = utils.find_optimal_cutoff_thresholds(y_train, y_train_pred)thresholds = utils.find_optimal_cutoff_thresholds_for_Gbeta(y_train, y_train_pred)
In addition to macro_auc and Fmax evaluate_experiment(y_true, y_pred, thresholds) will return F_beta_macro and G_beta_macro as proposed in the physionet-challenge.
Since bootstrapping results might take a while (even in parallel as in our code), we offer a flag for evaluation e.evaluate(bootstrap_eval=False) which just performs one single whole sample evaluation.
If you want to bootstrap your results: In each respective experiment-folder output/exp_*/ the bootstrapping ids for training,
testing and validation is stored as numpy-arrays containing lists of ids. Otherwise create manually with utils.get_appropriate_bootstrap_samples(y_train, n_bootstraping_samples). For sequential evaluation of those ids, the code might look like:
if experiment_name == 'exp_ICBEB':
thresholds = utils.find_optimal_cutoff_thresholds(y_train, y_train_pred)
else:
thresholds = None
train_bootstrap_samples = np.array(utils.get_appropriate_bootstrap_samples(y_train, n_bootstraping_samples))
tr_df = pd.concat([utils.evaluate_experiment(y_train[ids], y_train_pred[ids], thresholds) for ids in train_bootstrap_samples])
tr_df.quantile(0.05), tr_df.mean(), tr_df.quantile(0.95)In this jupyter notebook we provide a basic example of how to finetune our provided models on your custom dataset.
We encourage other authors to share their results on this dataset by submitting a PR. The evaluation proceeds as described in the manuscripts:
In all cases we restrict to classes with more than 50 entries in the whole dataset. The reported scores are test set scores (fold 10) as output
of the above evaluation procedure and should not be used for hyperparameter tuning or model selection. In the provided code, we use folds 1-8 for training,
fold 9 as validation set and fold 10 as test set. We encourage to submit also the prediction results (preds, targs, classes saved as numpy arrays preds_x.npy
and targs_x.npy and classes_x.npy) to ensure full reproducibility and to make source code and/or pretrained models available.
| Model | AUC ↓ | paper/source | code |
|---|---|---|---|
| inception1d | 0.925(08) | our work | this repo |
| xresnet1d101 | 0.925(07) | our work | this repo |
| resnet1d_wang | 0.919(08) | our work | this repo |
| fcn_wang | 0.918(08) | our work | this repo |
| lstm_bidir | 0.914(08) | our work | this repo |
| lstm | 0.907(08) | our work | this repo |
| Wavelet+NN | 0.849(13) | our work | this repo |
| Model | AUC ↓ | paper/source | code |
|---|---|---|---|
| xresnet1d101 | 0.937(08) | our work | this repo |
| resnet1d_wang | 0.936(08) | our work | this repo |
| lstm_bidir | 0.932(07) | our work | this repo |
| inception1d | 0.931(09) | our work | this repo |
| lstm | 0.927(08) | our work | this repo |
| fcn_wang | 0.926(10) | our work | this repo |
| Wavelet+NN | 0.855(15) | our work | this repo |
| Model | AUC ↓ | paper/source | code |
|---|---|---|---|
| inception1d | 0.930(10) | our work | this repo |
| xresnet1d101 | 0.929(14) | our work | this repo |
| lstm | 0.928(10) | our work | this repo |
| resnet1d_wang | 0.928(10) | our work | this repo |
| fcn_wang | 0.927(11) | our work | this repo |
| lstm_bidir | 0.923(12) | our work | this repo |
| Wavelet+NN | 0.859(16) | our work | this repo |
| Model | AUC ↓ | paper/source | code |
|---|---|---|---|
| resnet1d_wang | 0.930(05) | our work | this repo |
| xresnet1d101 | 0.928(05) | our work | this repo |
| lstm | 0.927(05) | our work | this repo |
| fcn_wang | 0.925(06) | our work | this repo |
| inception1d | 0.921(06) | our work | this repo |
| lstm_bidir | 0.921(06) | our work | this repo |
| Wavelet+NN | 0.874(07) | our work | this repo |
| Model | AUC ↓ | paper/source | code |
|---|---|---|---|
| inception1d | 0.899(22) | our work | this repo |
| xresnet1d101 | 0.896(12) | our work | this repo |
| resnet1d_wang | 0.880(15) | our work | this repo |
| lstm_bidir | 0.876(15) | our work | this repo |
| fcn_wang | 0.869(12) | our work | this repo |
| lstm | 0.851(15) | our work | this repo |
| Wavelet+NN | 0.757(29) | our work | this repo |
| Model | AUC ↓ | paper/source | code |
|---|---|---|---|
| xresnet1d101 | 0.957(19) | our work | this repo |
| inception1d | 0.953(13) | our work | this repo |
| lstm | 0.953(09) | our work | this repo |
| lstm_bidir | 0.949(11) | our work | this repo |
| resnet1d_wang | 0.946(10) | our work | this repo |
| fcn_wang | 0.931(08) | our work | this repo |
| Wavelet+NN | 0.890(24) | our work | this repo |
| Model | AUC ↓ | F_beta=2 | G_beta=2 | paper/source | code |
|---|---|---|---|---|---|
| xresnet1d101 | 0.974(05) | 0.819(30) | 0.602(37) | our work | this repo |
| resnet1d_wang | 0.969(06) | 0.803(31) | 0.586(37) | our work | this repo |
| lstm | 0.964(06) | 0.790(31) | 0.561(37) | our work | this repo |
| inception1d | 0.963(09) | 0.807(30) | 0.594(41) | our work | this repo |
| lstm_bidir | 0.959(11) | 0.796(31) | 0.573(36) | our work | this repo |
| fcn_wang | 0.957(08) | 0.787(31) | 0.563(37) | our work | this repo |
| Wavelet+NN | 0.905(14) | 0.665(34) | 0.405(36) | our work | this repo |
Please acknowledge our work by citing our preprint
@article{Strodthoff2020:ecgbenchmarking,
title={Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL},
author={Nils Strodthoff and Patrick Wagner and Tobias Schaeffter and Wojciech Samek},
journal={arXiv preprint 2004.13701},
year={2020},
eprint={2004.13701},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
For the PTB-XL dataset, please cite
@article{Wagner2020:ptbxl,
author={Patrick Wagner and Nils Strodthoff and Ralf-Dieter Bousseljot and Dieter Kreiseler and Fatima I. Lunze and Wojciech Samek and Tobias Schaeffter},
title={{PTB-XL}, a large publicly available electrocardiography dataset},
journal={Scientific Data},
year={2020},
note={awaiting publication}
}
@misc{Wagner2020:ptbxlphysionet,
title={{PTB-XL, a large publicly available electrocardiography dataset}},
author={Patrick Wagner and Nils Strodthoff and Ralf-Dieter Bousseljot and Wojciech Samek and Tobias Schaeffter},
doi={10.13026/qgmg-0d46},
year={2020},
journal={PhysioNet}
}
@article{Goldberger2020:physionet,
author = {Ary L. Goldberger and Luis A. N. Amaral and Leon Glass and Jeffrey M. Hausdorff and Plamen Ch. Ivanov and Roger G. Mark and Joseph E. Mietus and George B. Moody and Chung-Kang Peng and H. Eugene Stanley },
title = {{PhysioBank, PhysioToolkit, and PhysioNet}},
journal = {Circulation},
volume = {101},
number = {23},
pages = {e215-e220},
year = {2000},
doi = {10.1161/01.CIR.101.23.e215}
}
If you use the ICBEB challenge 2018 dataset please acknowledge
@article{liu2018:icbeb,
doi = {10.1166/jmihi.2018.2442},
year = {2018},
month = sep,
publisher = {American Scientific Publishers},
volume = {8},
number = {7},
pages = {1368--1373},
author = {Feifei Liu and Chengyu Liu and Lina Zhao and Xiangyu Zhang and Xiaoling Wu and Xiaoyan Xu and Yulin Liu and Caiyun Ma and Shoushui Wei and Zhiqiang He and Jianqing Li and Eddie Ng Yin Kwee},
title = {{An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection}},
journal = {Journal of Medical Imaging and Health Informatics}
}