├── dnn_predicts ├── model.npy ├── other_seeds │ ├── model_1.npy │ ├── model_2.npy │ ├── model_3.npy │ ├── model_4.npy │ ├── model_5.npy │ ├── model_6.npy │ ├── model_7.npy │ ├── model_8.npy │ ├── model_9.npy │ └── model_10.npy ├── other_splits │ ├── model_date_order.npy │ ├── model_normal_order.npy │ └── model_individual_patients.npy └── README.md ├── outputs ├── tables │ ├── kappa.xlsx │ ├── scores.xlsx │ ├── mcnemar.xlsx │ ├── confusion matrices.xlsx │ ├── kappas_annotators_and_DNN.xlsx │ ├── kappas_annotators_and_DNN.csv │ ├── kappa.csv │ ├── mcnemar.csv │ ├── confusion matrices.csv │ └── scores.csv └── figures │ ├── precision_recall_AF.pdf │ ├── precision_recall_SB.pdf │ ├── precision_recall_ST.pdf │ ├── precision_recall_LBBB.pdf │ ├── precision_recall_RBBB.pdf │ ├── boxplot_bootstrap_Recall.pdf │ ├── precision_recall_1dAVb.pdf │ ├── boxplot_bootstrap_F1 score.pdf │ ├── boxplot_F1 score_other_splits.pdf │ ├── boxplot_bootstrap_Precision.pdf │ ├── boxplot_bootstrap_Specificity.pdf │ └── boxplot_bootstrap_other_splits_F1 score.pdf ├── requirements.txt ├── LICENSE ├── predict.py ├── datasets.py ├── .gitignore ├── train.py ├── data ├── README.md ├── attributes.csv └── annotations │ ├── cardiologist1.csv │ ├── cardiologist2.csv │ ├── gold_standard.csv │ ├── medical_students.csv │ ├── cardiology_residents.csv │ ├── emergency_residents.csv │ └── dnn.csv ├── README.md ├── model.py └── generate_figures_and_tables.py /dnn_predicts/model.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/antonior92/automatic-ecg-diagnosis/HEAD/dnn_predicts/model.npy -------------------------------------------------------------------------------- /outputs/tables/kappa.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/antonior92/automatic-ecg-diagnosis/HEAD/outputs/tables/kappa.xlsx 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/dnn_predicts/other_splits/model_individual_patients.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/antonior92/automatic-ecg-diagnosis/HEAD/dnn_predicts/other_splits/model_individual_patients.npy -------------------------------------------------------------------------------- /outputs/figures/boxplot_bootstrap_other_splits_F1 score.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/antonior92/automatic-ecg-diagnosis/HEAD/outputs/figures/boxplot_bootstrap_other_splits_F1 score.pdf -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy>=1.14.3 2 | pandas>=0.22 3 | tensorflow==2.2 4 | h5py>=2.8 5 | xmljson>=0.1.9 6 | scipy>=1.1 7 | scikit-learn>=0.20 8 | tqdm>=4.26 9 | xarray>=0.11.2 10 | seaborn>=0.9 11 | openpyxl>=3.0 12 | -------------------------------------------------------------------------------- /outputs/tables/kappas_annotators_and_DNN.csv: -------------------------------------------------------------------------------- 1 | ,1dAVb,RBBB,LBBB,SB,AF,ST 2 | DNN vs Cert. cardiol. 1,0.758,0.928,0.964,0.770,0.696,0.847 3 | DNN vs Certif. cardiol. 2,0.852,0.942,1.000,0.770,0.746,0.884 4 | Cert. cardiol. 1 vs Certif. cardiol. 2,0.741,0.955,0.964,0.844,0.831,0.902 5 | -------------------------------------------------------------------------------- /outputs/tables/kappa.csv: -------------------------------------------------------------------------------- 1 | ,1dAVb,RBBB,LBBB,SB,AF,ST 2 | DNN vs cardio.,0.656,0.917,0.945,0.830,0.780,0.864 3 | DNN vs emerg.,0.684,0.792,0.909,0.796,0.595,0.930 4 | DNN vs stud.,0.642,0.928,0.912,0.760,0.574,0.855 5 | cardio. vs emerg.,0.656,0.824,0.923,0.912,0.515,0.847 6 | cardio. vs stud.,0.612,0.871,0.889,0.880,0.700,0.792 7 | emerg. vs stud.,0.615,0.799,0.852,0.907,0.508,0.897 8 | -------------------------------------------------------------------------------- /outputs/tables/mcnemar.csv: -------------------------------------------------------------------------------- 1 | ,1dAVb,RBBB,LBBB,SB,AF,ST 2 | DNN vs cardio.,0.225,0.414,0.083,1.000,0.180,0.096 3 | DNN vs emerg.,0.007,0.166,0.025,0.705,0.157,0.655 4 | DNN vs stud.,0.009,0.655,0.025,0.157,0.052,0.058 5 | cardio. vs emerg.,0.108,0.366,0.317,0.564,0.763,0.206 6 | cardio. vs stud.,0.102,0.739,0.414,0.046,0.206,0.782 7 | emerg. vs stud.,0.853,0.248,1.000,0.083,0.439,0.059 8 | -------------------------------------------------------------------------------- /outputs/tables/confusion matrices.csv: -------------------------------------------------------------------------------- 1 | predictor,,DNN,DNN,cardio.,cardio.,emerg.,emerg.,stud.,stud. 2 | predicted label,,not present,present,not present,present,not present,present,not present,present 3 | diagnosis,true label,,,,,,,, 4 | 1dAVb,not present,795,4,797,2,786,13,782,17 5 | 1dAVb,present,2,26,9,19,5,23,2,26 6 | AF,not present,814,0,811,3,812,2,805,9 7 | AF,present,3,10,3,10,5,8,1,12 8 | LBBB,not present,797,0,797,0,796,1,795,2 9 | LBBB,present,0,30,3,27,4,26,3,27 10 | RBBB,not present,789,4,788,5,792,1,790,3 11 | RBBB,present,0,34,1,33,8,26,2,32 12 | SB,not present,808,3,808,3,808,3,807,4 13 | SB,present,1,15,1,15,2,14,4,12 14 | ST,not present,788,2,789,1,788,2,787,3 15 | ST,present,1,36,7,30,2,35,6,31 16 | -------------------------------------------------------------------------------- /outputs/tables/scores.csv: -------------------------------------------------------------------------------- 1 | ,Precision,Precision,Precision,Precision,Recall,Recall,Recall,Recall,Specificity,Specificity,Specificity,Specificity,F1 score,F1 score,F1 score,F1 score 2 | ,DNN,cardio.,emerg.,stud.,DNN,cardio.,emerg.,stud.,DNN,cardio.,emerg.,stud.,DNN,cardio.,emerg.,stud. 3 | 1dAVb,0.867,0.905,0.639,0.605,0.929,0.679,0.821,0.929,0.995,0.997,0.984,0.979,0.897,0.776,0.719,0.732 4 | RBBB,0.895,0.868,0.963,0.914,1.000,0.971,0.765,0.941,0.995,0.994,0.999,0.996,0.944,0.917,0.852,0.928 5 | LBBB,1.000,1.000,0.963,0.931,1.000,0.900,0.867,0.900,1.000,1.000,0.999,0.997,1.000,0.947,0.912,0.915 6 | SB,0.833,0.833,0.824,0.750,0.938,0.938,0.875,0.750,0.996,0.996,0.996,0.995,0.882,0.882,0.848,0.750 7 | AF,1.000,0.769,0.800,0.571,0.769,0.769,0.615,0.923,1.000,0.996,0.998,0.989,0.870,0.769,0.696,0.706 8 | ST,0.947,0.968,0.946,0.912,0.973,0.811,0.946,0.838,0.997,0.999,0.997,0.996,0.960,0.882,0.946,0.873 9 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Antonio Horta Ribeiro 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import warnings 3 | import argparse 4 | warnings.filterwarnings("ignore") 5 | from tensorflow.keras.models import load_model 6 | from tensorflow.keras.optimizers import Adam 7 | from datasets import ECGSequence 8 | 9 | 10 | if __name__ == '__main__': 11 | parser = argparse.ArgumentParser(description='Get performance on test set from hdf5') 12 | parser.add_argument('path_to_hdf5', type=str, 13 | help='path to hdf5 file containing tracings') 14 | parser.add_argument('path_to_model', # or model_date_order.hdf5 15 | help='file containing training model.') 16 | parser.add_argument('--dataset_name', type=str, default='tracings', 17 | help='name of the hdf5 dataset containing tracings') 18 | parser.add_argument('--output_file', default="./dnn_output.npy", # or predictions_date_order.csv 19 | help='output csv file.') 20 | parser.add_argument('-bs', type=int, default=32, 21 | help='Batch size.') 22 | 23 | args, unk = parser.parse_known_args() 24 | if unk: 25 | warnings.warn("Unknown arguments:" + str(unk) + ".") 26 | 27 | # Import data 28 | seq = ECGSequence(args.path_to_hdf5, args.dataset_name, batch_size=args.bs) 29 | # Import model 30 | model = load_model(args.path_to_model, compile=False) 31 | model.compile(loss='binary_crossentropy', optimizer=Adam()) 32 | y_score = model.predict(seq, verbose=1) 33 | 34 | # Generate dataframe 35 | np.save(args.output_file, y_score) 36 | 37 | print("Output predictions saved") 38 | -------------------------------------------------------------------------------- /datasets.py: -------------------------------------------------------------------------------- 1 | import h5py 2 | import math 3 | import pandas as pd 4 | from tensorflow.keras.utils import Sequence 5 | import numpy as np 6 | 7 | 8 | class ECGSequence(Sequence): 9 | @classmethod 10 | def get_train_and_val(cls, path_to_hdf5, hdf5_dset, path_to_csv, batch_size=8, val_split=0.02): 11 | n_samples = len(pd.read_csv(path_to_csv)) 12 | n_train = math.ceil(n_samples*(1-val_split)) 13 | train_seq = cls(path_to_hdf5, hdf5_dset, path_to_csv, batch_size, end_idx=n_train) 14 | valid_seq = cls(path_to_hdf5, hdf5_dset, path_to_csv, batch_size, start_idx=n_train) 15 | return train_seq, valid_seq 16 | 17 | def __init__(self, path_to_hdf5, hdf5_dset, path_to_csv=None, batch_size=8, 18 | start_idx=0, end_idx=None): 19 | if path_to_csv is None: 20 | self.y = None 21 | else: 22 | self.y = pd.read_csv(path_to_csv).values 23 | # Get tracings 24 | self.f = h5py.File(path_to_hdf5, "r") 25 | self.x = self.f[hdf5_dset] 26 | self.batch_size = batch_size 27 | if end_idx is None: 28 | end_idx = len(self.x) 29 | self.start_idx = start_idx 30 | self.end_idx = end_idx 31 | 32 | @property 33 | def n_classes(self): 34 | return self.y.shape[1] 35 | 36 | def __getitem__(self, idx): 37 | start = self.start_idx + idx * self.batch_size 38 | end = min(start + self.batch_size, self.end_idx) 39 | if self.y is None: 40 | return np.array(self.x[start:end, :, :]) 41 | else: 42 | return np.array(self.x[start:end, :, :]), np.array(self.y[start:end]) 43 | 44 | def __len__(self): 45 | return math.ceil((self.end_idx - self.start_idx) / self.batch_size) 46 | 47 | def __del__(self): 48 | self.f.close() 49 | -------------------------------------------------------------------------------- /dnn_predicts/README.md: -------------------------------------------------------------------------------- 1 | # Model predictions on the test set 2 | This folder contain the deep neural network predictions on the test set. All files are in 3 | the format `.npy` and can be read using `numpy.load()`. Each one should contain a 4 | 5 | All the content within this folder can be generate using the following sequence of commands: 6 | 7 | (without a GPU it should take about 25 minutes. With GPU acceleration it should take 8 | less then one minute) 9 | 10 | ```bash 11 | cd /path/to/automatic-ecg-diagnosis 12 | PFOLDER="./dnn_predicts" 13 | MFOLDER="./model" 14 | DFOLDER="./data" 15 | 16 | # To generate the predictions on the test set corresponding to the main model used allong the paper use: 17 | 18 | python predict.py $DFOLDER/ecg_tracings.hdf5 $MFOLDER/model.hdf5 --output_file $PFOLDER/model.npy 19 | 20 | 21 | # We also train several networks with the same architecture and configuration 22 | # but with different initial seeds. In order to generate the neural network 23 | # prediction on the test set for each of these models: 24 | 25 | mkdir $FNAME/other_seeds 26 | for n in 1 2 3 4 5 6 7 8 9 10 27 | do 28 | python predict.py $DFOLDER/ecg_tracings.hdf5 $MFOLDER/other_seeds/model_$n.hdf5 --output_file $PFOLDER/other_seeds/model_$n.npy 29 | done 30 | 31 | 32 | # Finally, to asses the effect of how we structure our problem, we have considered alternative s 33 | # cenarios where we use 90\%-5\%-5\% splits, stratified randomly, 34 | # by patient or in chronological order. The predictions of those models in the test set 35 | # can be obtained using: 36 | 37 | mkdir $PFOLDER/other_splits 38 | for n in date_order individual_patients normal_order 39 | do 40 | python predict.py $DFOLDER/ecg_tracings.hdf5 $MFOLDER/other_splits/model_$n.hdf5 --output_file $PFOLDER/other_splits/model_$n.npy 41 | done 42 | ``` 43 | 44 | Where the `DFOLDER` should give the path to the folder containing the test dataset and `MFOLDER` should point to the 45 | folder containing pre-trained models. The test dataset can be downloaded from [here](https://doi.org/10.5281/zenodo.3625006) and the 46 | pretrained models can be downloaded from here [here](https://doi.org/10.5281/zenodo.3625017) -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # Avoid large files 132 | *.hdf5 133 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | from tensorflow.keras.optimizers import Adam 2 | from tensorflow.keras.callbacks import (ModelCheckpoint, TensorBoard, ReduceLROnPlateau, 3 | CSVLogger, EarlyStopping) 4 | from model import get_model 5 | import argparse 6 | from datasets import ECGSequence 7 | 8 | if __name__ == "__main__": 9 | # Get data and train 10 | parser = argparse.ArgumentParser(description='Train neural network.') 11 | parser.add_argument('path_to_hdf5', type=str, 12 | help='path to hdf5 file containing tracings') 13 | parser.add_argument('path_to_csv', type=str, 14 | help='path to csv file containing annotations') 15 | parser.add_argument('--val_split', type=float, default=0.02, 16 | help='number between 0 and 1 determining how much of' 17 | ' the data is to be used for validation. The remaining ' 18 | 'is used for validation. Default: 0.02') 19 | parser.add_argument('--dataset_name', type=str, default='tracings', 20 | help='name of the hdf5 dataset containing tracings') 21 | args = parser.parse_args() 22 | # Optimization settings 23 | loss = 'binary_crossentropy' 24 | lr = 0.001 25 | batch_size = 64 26 | opt = Adam(lr) 27 | callbacks = [ReduceLROnPlateau(monitor='val_loss', 28 | factor=0.1, 29 | patience=7, 30 | min_lr=lr / 100), 31 | EarlyStopping(patience=9, # Patience should be larger than the one in ReduceLROnPlateau 32 | min_delta=0.00001)] 33 | 34 | train_seq, valid_seq = ECGSequence.get_train_and_val( 35 | args.path_to_hdf5, args.dataset_name, args.path_to_csv, batch_size, args.val_split) 36 | 37 | # If you are continuing an interrupted section, uncomment line bellow: 38 | # model = keras.models.load_model(PATH_TO_PREV_MODEL, compile=False) 39 | model = get_model(train_seq.n_classes) 40 | model.compile(loss=loss, optimizer=opt) 41 | # Create log 42 | callbacks += [TensorBoard(log_dir='./logs', write_graph=False), 43 | CSVLogger('training.log', append=False)] # Change append to true if continuing training 44 | # Save the BEST and LAST model 45 | callbacks += [ModelCheckpoint('./backup_model_last.hdf5'), 46 | ModelCheckpoint('./backup_model_best.hdf5', save_best_only=True)] 47 | # Train neural network 48 | history = model.fit(train_seq, 49 | epochs=70, 50 | initial_epoch=0, # If you are continuing a interrupted section change here 51 | callbacks=callbacks, 52 | validation_data=valid_seq, 53 | verbose=1) 54 | # Save final result 55 | model.save("./final_model.hdf5") 56 | -------------------------------------------------------------------------------- /data/README.md: -------------------------------------------------------------------------------- 1 | # Annotated 12 lead ECG dataset 2 | 3 | Contain 827 ECG tracings from different patients, annotated by several cardiologists, residents and medical students. 4 | It is used as test set on the paper: 5 | "Automatic diagnosis of the 12-lead ECG using a deep neural network". 6 | https://www.nature.com/articles/s41467-020-15432-4. 7 | 8 | It contain annotations about 6 different ECGs abnormalities: 9 | - 1st degree AV block (1dAVb); 10 | - right bundle branch block (RBBB); 11 | - left bundle branch block (LBBB); 12 | - sinus bradycardia (SB); 13 | - atrial fibrillation (AF); and, 14 | - sinus tachycardia (ST). 15 | 16 | Companion python scripts are available in: 17 | https://github.com/antonior92/automatic-ecg-diagnosis 18 | 19 | -------- 20 | 21 | Citation 22 | ``` 23 | Ribeiro, A.H., Ribeiro, M.H., Paixão, G.M.M. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. 24 | Nat Commun 11, 1760 (2020). https://doi.org/10.1038/s41467-020-15432-4 25 | ``` 26 | 27 | Bibtex: 28 | ``` 29 | @article{ribeiro_automatic_2020, 30 | title = {Automatic Diagnosis of the 12-Lead {{ECG}} Using a Deep Neural Network}, 31 | author = {Ribeiro, Ant{\^o}nio H. and Ribeiro, Manoel Horta and Paix{\~a}o, Gabriela M. M. and Oliveira, Derick M. and Gomes, Paulo R. and Canazart, J{\'e}ssica A. and Ferreira, Milton P. S. and Andersson, Carl R. and Macfarlane, Peter W. and Meira Jr., Wagner and Sch{\"o}n, Thomas B. and Ribeiro, Antonio Luiz P.}, 32 | year = {2020}, 33 | volume = {11}, 34 | pages = {1760}, 35 | doi = {https://doi.org/10.1038/s41467-020-15432-4}, 36 | journal = {Nature Communications}, 37 | number = {1} 38 | } 39 | ``` 40 | ----- 41 | 42 | 43 | ## Folder content: 44 | 45 | - `ecg_tracings.hdf5`: this file is not available on github repository because of the size. But it can be downloaded 46 | [here](https://doi.org/10.5281/zenodo.3625006). The HDF5 file containing a single dataset named `tracings`. This dataset is a 47 | `(827, 4096, 12)` tensor. The first dimension correspond to the 827 different exams from different 48 | patients; the second dimension correspond to the 4096 signal samples; the third dimension to the 12 49 | different leads of the ECG exams in the following order: 50 | `{DI, DII, DIII, AVL, AVF, AVR, V1, V2, V3, V4, V5, V6}`. 51 | 52 | The signals are sampled at 400 Hz. Some signals originally have a duration of 53 | 10 seconds (10 * 400 = 4000 samples) and others of 7 seconds (7 * 400 = 2800 samples). 54 | In order to make them all have the same size (4096 samples) we fill them with zeros 55 | on both sizes. For instance, for a 7 seconds ECG signal with 2800 samples we include 648 56 | samples at the beginning and 648 samples at the end, yielding 4096 samples that are them saved 57 | in the hdf5 dataset. All signal are represented as floating point numbers at the scale 1e-4V: so it should 58 | be multiplied by 1000 in order to obtain the signals in V. 59 | 60 | In python, one can read this file using the following sequence: 61 | ```python 62 | import h5py 63 | with h5py.File(args.tracings, "r") as f: 64 | x = np.array(f['tracings']) 65 | ``` 66 | 67 | - The file `attributes.csv` contain basic patient attributes: sex (M or F) and age. It 68 | contain 827 lines (plus the header). The i-th tracing in `ecg_tracings.hdf5` correspond to the i-th line. 69 | - `annotations/`: folder containing annotations csv format. Each csv file contain 827 lines (plus the header). 70 | The i-th line correspond to the i-th tracing in `ecg_tracings.hdf5` correspond to the in all csv files. 71 | The csv files all have 6 columns `1dAVb, RBBB, LBBB, SB, AF, ST` 72 | corresponding to weather the annotator have detect the abnormality in the ECG (`=1`) or not (`=0`). 73 | 1. `cardiologist[1,2].csv` contain annotations from two different cardiologist. 74 | 2. `gold_standard.csv` gold standard annotation for this test dataset. When the cardiologist 1 and cardiologist 2 75 | agree, the common diagnosis was considered as gold standard. In cases where there was any disagreement, a 76 | third senior specialist, aware of the annotations from the other two, decided the diagnosis. 77 | 3. `dnn.csv` prediction from the deep neural network described in 78 | "Automatic Diagnosis of the Short-Duration12-Lead ECG using a Deep Neural Network". THe threshold is set in such way 79 | it maximizes the F1 score. 80 | 4. `cardiology_residents.csv` annotations from two 4th year cardiology residents (each annotated half of the dataset). 81 | 5. `emergency_residents.csv` annotations from two 3rd year emergency residents (each annotated half of the dataset). 82 | 6. `medical_students.csv` annotations from two 5th year medical students (each annotated half of the dataset). 83 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Automatic ECG diagnosis using a deep neural network 2 | Scripts and modules for training and testing deep neural networks for ECG automatic classification. 3 | Companion code to the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network". 4 | https://www.nature.com/articles/s41467-020-15432-4. 5 | 6 | -------- 7 | 8 | Citation: 9 | ``` 10 | Ribeiro, A.H., Ribeiro, M.H., Paixão, G.M.M. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. 11 | Nat Commun 11, 1760 (2020). https://doi.org/10.1038/s41467-020-15432-4 12 | ``` 13 | 14 | Bibtex: 15 | ``` 16 | @article{ribeiro_automatic_2020, 17 | title = {Automatic Diagnosis of the 12-Lead {{ECG}} Using a Deep Neural Network}, 18 | author = {Ribeiro, Ant{\^o}nio H. and Ribeiro, Manoel Horta and Paix{\~a}o, Gabriela M. M. and Oliveira, Derick M. and Gomes, Paulo R. and Canazart, J{\'e}ssica A. and Ferreira, Milton P. S. and Andersson, Carl R. and Macfarlane, Peter W. and Meira Jr., Wagner and Sch{\"o}n, Thomas B. and Ribeiro, Antonio Luiz P.}, 19 | year = {2020}, 20 | volume = {11}, 21 | pages = {1760}, 22 | doi = {https://doi.org/10.1038/s41467-020-15432-4}, 23 | journal = {Nature Communications}, 24 | number = {1} 25 | } 26 | ``` 27 | ----- 28 | 29 | ## Requirements 30 | 31 | This code was tested on Python 3 with Tensorflow `2.2`. There is an older branch ([`tensorflow-v1`](https://github.com/antonior92/automatic-ecg-diagnosis/tree/tensorflow-v1)) that 32 | contain the code implementation for Tensorflow `1.15`. 33 | 34 | **For pytorch users:** If you are interested in a pytorch implementation, take a look in the repository: https://github.com/antonior92/ecg-age-prediction. 35 | There we provide a implementation in PyTorch of the same resnet-based model. The problem there is the age prediction from the ECG, nontheless simple modifications should suffice for dealing with abnormality classification. 36 | 37 | ## Model 38 | 39 | The model used in the paper is a residual neural. The neural network architecture implementation in Keras is available in ``model.py``. To print a summary of the model layers run: 40 | ```bash 41 | $ python model.py 42 | ``` 43 | 44 | ![resnet](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-15432-4/MediaObjects/41467_2020_15432_Fig3_HTML.png?as=webp) 45 | 46 | The model receives an input tensor with dimension `(N, 4096, 12)`, and returns an output tensor with dimension `(N, 6)`, 47 | for which `N` is the batch size. 48 | 49 | The model can be trained using the script `train.py`. Alternatively, 50 | pre-trained weighs for the models described in the paper are also 51 | available in: https://doi.org/10.5281/zenodo.3625017. Or in the mirror dropbox 52 | link [here](https://www.dropbox.com/s/5ar6j8u9v9a0rmh/model.zip?dl=0). 53 | Using the command line, the weights can be downloaded using 54 | ``` 55 | wget https://www.dropbox.com/s/5ar6j8u9v9a0rmh/model.zip?dl=0 -O model.zip 56 | unzip model.zip 57 | ``` 58 | 59 | - **input**: `shape = (N, 4096, 12)`. The input tensor should contain the `4096` points of the ECG tracings 60 | sampled at `400Hz` (i.e., a signal of approximately 10 seconds). Both in the training and in the test set, when the 61 | signal was not long enough, we filled the signal with zeros, so 4096 points were attained. The last dimension of the 62 | tensor contains points of the 12 different leads. The leads are ordered in the following order: 63 | `{DI, DII, DIII, AVR, AVL, AVF, V1, V2, V3, V4, V5, V6}`. All signal are represented as 64 | 32 bits floating point numbers at the scale 1e-4V: so if the signal is in V it should be multiplied by 65 | 1000 before feeding it to the neural network model. 66 | 67 | 68 | - **output**: `shape = (N, 6)`. Each entry contains a probability between 0 and 1, and can be understood as the 69 | probability of a given abnormality to be present. The abnormalities it predicts are **(in that order)**: 1st degree AV block(1dAVb), 70 | right bundle branch block (RBBB), left bundle branch block (LBBB), sinus bradycardia (SB), atrial fibrillation (AF), 71 | sinus tachycardia (ST). The abnormalities are not mutually exclusive, so the probabilities do not necessarily 72 | sum to one. 73 | 74 | ![abnormalities](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-15432-4/MediaObjects/41467_2020_15432_Fig1_HTML.png?as=webp) 75 | 76 | ## Datasets 77 | 78 | - The testing dataset described in the paper can be downloaded in: 79 | [doi: 10.5281/zenodo.3625006](https://doi.org/10.5281/zenodo.3625006). There is also a mirror 80 | dropbox link [here](https://www.dropbox.com/s/p3vd3plcbu9sf1o/data.zip?dl=0). 81 | Using the command line: 82 | ``` 83 | wget https://www.dropbox.com/s/p3vd3plcbu9sf1o/data.zip?dl=0 -O data.zip 84 | unzip data.zip 85 | ``` 86 | - Part of the training data (the CODE-15\% dataset) is openly available in: [doi: 10.5281/zenodo.4916206 ](https://doi.org/10.5281/zenodo.4916206). 87 | - The full CODE dataset that was used for training is available upon request for research purposes: [doi: 10.17044/scilifelab.15169716](https://doi.org/10.17044/scilifelab.15169716) 88 | 89 | ## Scripts 90 | 91 | - ``train.py``: Script for training the neural network. To train the neural network run: 92 | ```bash 93 | $ python train.py PATH_TO_HDF5 PATH_TO_CSV 94 | ``` 95 | Pre-trained models obtained using such script can be downloaded from [here](https://doi.org/10.5281/zenodo.3625017) 96 | 97 | 98 | - ``predict.py``: Script for generating the neural network predictions on a given dataset. 99 | ```bash 100 | $ python predict.py PATH_TO_HDF5_ECG_TRACINGS PATH_TO_MODEL --ouput_file PATH_TO_OUTPUT_FILE 101 | ``` 102 | The folder `./dnn_predicts` contain the output obtained by applying this script to the models available in 103 | [here](https://doi.org/10.5281/zenodo.3625017) to make the predictions on tracings from 104 | [this test dataset](https://doi.org/10.5281/zenodo.3625006). 105 | 106 | 107 | - ``generate_figures_and_tables.py``: Generate figures and tables from the paper "Automatic Diagnosis o 108 | the Short-Duration12-Lead ECG using a Deep Neural Network". Make sure to execute the script from the root folder, 109 | so all relative paths are correct. So first run: 110 | ``` 111 | $ cd /path/to/automatic-ecg-diagnosis 112 | ``` 113 | Then the script 114 | ```bash 115 | $ python generate_figures_and_tables.py 116 | ``` 117 | It should generate the tables and figure in the folder `outputs/` 118 | 119 | - ``model.py``: Auxiliary module that defines the architecture of the deep neural network. 120 | To print a summary of the model layers run: 121 | ```bash 122 | $ python model.py 123 | ``` 124 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | from tensorflow.keras.layers import ( 2 | Input, Conv1D, MaxPooling1D, Dropout, BatchNormalization, Activation, Add, Flatten, Dense) 3 | from tensorflow.keras.models import Model 4 | import numpy as np 5 | 6 | 7 | class ResidualUnit(object): 8 | """Residual unit block (unidimensional). 9 | Parameters 10 | ---------- 11 | n_samples_out: int 12 | Number of output samples. 13 | n_filters_out: int 14 | Number of output filters. 15 | kernel_initializer: str, optional 16 | Initializer for the weights matrices. See Keras initializers. By default it uses 17 | 'he_normal'. 18 | dropout_keep_prob: float [0, 1), optional 19 | Dropout rate used in all Dropout layers. Default is 0.8 20 | kernel_size: int, optional 21 | Kernel size for convolutional layers. Default is 17. 22 | preactivation: bool, optional 23 | When preactivation is true use full preactivation architecture proposed 24 | in [1]. Otherwise, use architecture proposed in the original ResNet 25 | paper [2]. By default it is true. 26 | postactivation_bn: bool, optional 27 | Defines if you use batch normalization before or after the activation layer (there 28 | seems to be some advantages in some cases: 29 | https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md). 30 | If true, the batch normalization is used before the activation 31 | function, otherwise the activation comes first, as it is usually done. 32 | By default it is false. 33 | activation_function: string, optional 34 | Keras activation function to be used. By default 'relu'. 35 | References 36 | ---------- 37 | .. [1] K. He, X. Zhang, S. Ren, and J. Sun, "Identity Mappings in Deep Residual Networks," 38 | arXiv:1603.05027 [cs], Mar. 2016. https://arxiv.org/pdf/1603.05027.pdf. 39 | .. [2] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference 40 | on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. https://arxiv.org/pdf/1512.03385.pdf 41 | """ 42 | 43 | def __init__(self, n_samples_out, n_filters_out, kernel_initializer='he_normal', 44 | dropout_keep_prob=0.8, kernel_size=17, preactivation=True, 45 | postactivation_bn=False, activation_function='relu'): 46 | self.n_samples_out = n_samples_out 47 | self.n_filters_out = n_filters_out 48 | self.kernel_initializer = kernel_initializer 49 | self.dropout_rate = 1 - dropout_keep_prob 50 | self.kernel_size = kernel_size 51 | self.preactivation = preactivation 52 | self.postactivation_bn = postactivation_bn 53 | self.activation_function = activation_function 54 | 55 | def _skip_connection(self, y, downsample, n_filters_in): 56 | """Implement skip connection.""" 57 | # Deal with downsampling 58 | if downsample > 1: 59 | y = MaxPooling1D(downsample, strides=downsample, padding='same')(y) 60 | elif downsample == 1: 61 | y = y 62 | else: 63 | raise ValueError("Number of samples should always decrease.") 64 | # Deal with n_filters dimension increase 65 | if n_filters_in != self.n_filters_out: 66 | # This is one of the two alternatives presented in ResNet paper 67 | # Other option is to just fill the matrix with zeros. 68 | y = Conv1D(self.n_filters_out, 1, padding='same', 69 | use_bias=False, kernel_initializer=self.kernel_initializer)(y) 70 | return y 71 | 72 | def _batch_norm_plus_activation(self, x): 73 | if self.postactivation_bn: 74 | x = Activation(self.activation_function)(x) 75 | x = BatchNormalization(center=False, scale=False)(x) 76 | else: 77 | x = BatchNormalization()(x) 78 | x = Activation(self.activation_function)(x) 79 | return x 80 | 81 | def __call__(self, inputs): 82 | """Residual unit.""" 83 | x, y = inputs 84 | n_samples_in = y.shape[1] 85 | downsample = n_samples_in // self.n_samples_out 86 | n_filters_in = y.shape[2] 87 | y = self._skip_connection(y, downsample, n_filters_in) 88 | # 1st layer 89 | x = Conv1D(self.n_filters_out, self.kernel_size, padding='same', 90 | use_bias=False, kernel_initializer=self.kernel_initializer)(x) 91 | x = self._batch_norm_plus_activation(x) 92 | if self.dropout_rate > 0: 93 | x = Dropout(self.dropout_rate)(x) 94 | 95 | # 2nd layer 96 | x = Conv1D(self.n_filters_out, self.kernel_size, strides=downsample, 97 | padding='same', use_bias=False, 98 | kernel_initializer=self.kernel_initializer)(x) 99 | if self.preactivation: 100 | x = Add()([x, y]) # Sum skip connection and main connection 101 | y = x 102 | x = self._batch_norm_plus_activation(x) 103 | if self.dropout_rate > 0: 104 | x = Dropout(self.dropout_rate)(x) 105 | else: 106 | x = BatchNormalization()(x) 107 | x = Add()([x, y]) # Sum skip connection and main connection 108 | x = Activation(self.activation_function)(x) 109 | if self.dropout_rate > 0: 110 | x = Dropout(self.dropout_rate)(x) 111 | y = x 112 | return [x, y] 113 | 114 | 115 | def get_model(n_classes, last_layer='sigmoid'): 116 | kernel_size = 16 117 | kernel_initializer = 'he_normal' 118 | signal = Input(shape=(4096, 12), dtype=np.float32, name='signal') 119 | x = signal 120 | x = Conv1D(64, kernel_size, padding='same', use_bias=False, 121 | kernel_initializer=kernel_initializer)(x) 122 | x = BatchNormalization()(x) 123 | x = Activation('relu')(x) 124 | x, y = ResidualUnit(1024, 128, kernel_size=kernel_size, 125 | kernel_initializer=kernel_initializer)([x, x]) 126 | x, y = ResidualUnit(256, 196, kernel_size=kernel_size, 127 | kernel_initializer=kernel_initializer)([x, y]) 128 | x, y = ResidualUnit(64, 256, kernel_size=kernel_size, 129 | kernel_initializer=kernel_initializer)([x, y]) 130 | x, _ = ResidualUnit(16, 320, kernel_size=kernel_size, 131 | kernel_initializer=kernel_initializer)([x, y]) 132 | x = Flatten()(x) 133 | diagn = Dense(n_classes, activation=last_layer, kernel_initializer=kernel_initializer)(x) 134 | model = Model(signal, diagn) 135 | return model 136 | 137 | 138 | if __name__ == "__main__": 139 | model = get_model(6) 140 | model.summary() 141 | -------------------------------------------------------------------------------- /data/attributes.csv: -------------------------------------------------------------------------------- 1 | age,sex 2 | 34,M 3 | 70,F 4 | 52,F 5 | 41,F 6 | 32,F 7 | 45,F 8 | 47,M 9 | 51,F 10 | 49,F 11 | 62,M 12 | 51,F 13 | 37,F 14 | 42,M 15 | 76,F 16 | 62,F 17 | 77,F 18 | 77,F 19 | 36,F 20 | 68,F 21 | 44,F 22 | 34,F 23 | 42,F 24 | 66,F 25 | 80,F 26 | 33,F 27 | 41,F 28 | 65,F 29 | 68,F 30 | 63,F 31 | 33,F 32 | 38,F 33 | 88,M 34 | 67,M 35 | 78,M 36 | 63,F 37 | 64,F 38 | 29,F 39 | 49,F 40 | 70,M 41 | 63,F 42 | 17,M 43 | 50,F 44 | 61,M 45 | 67,M 46 | 70,F 47 | 49,F 48 | 48,F 49 | 68,F 50 | 73,M 51 | 45,F 52 | 64,F 53 | 23,M 54 | 39,F 55 | 84,F 56 | 54,M 57 | 74,F 58 | 35,F 59 | 80,F 60 | 70,M 61 | 72,F 62 | 69,M 63 | 68,M 64 | 57,M 65 | 51,F 66 | 85,M 67 | 66,M 68 | 75,F 69 | 70,F 70 | 79,M 71 | 68,F 72 | 42,F 73 | 75,M 74 | 59,M 75 | 80,M 76 | 43,F 77 | 70,M 78 | 97,F 79 | 62,F 80 | 43,M 81 | 25,M 82 | 60,F 83 | 47,M 84 | 58,M 85 | 39,M 86 | 78,M 87 | 48,M 88 | 83,M 89 | 48,F 90 | 66,F 91 | 79,F 92 | 43,F 93 | 77,M 94 | 38,F 95 | 62,F 96 | 46,F 97 | 72,F 98 | 93,F 99 | 47,F 100 | 67,M 101 | 83,M 102 | 50,M 103 | 50,M 104 | 53,F 105 | 46,F 106 | 85,F 107 | 36,F 108 | 62,M 109 | 72,F 110 | 62,F 111 | 32,M 112 | 65,M 113 | 57,F 114 | 30,M 115 | 62,M 116 | 50,F 117 | 62,F 118 | 45,F 119 | 22,M 120 | 70,F 121 | 48,F 122 | 59,F 123 | 33,F 124 | 21,M 125 | 48,F 126 | 69,M 127 | 57,F 128 | 71,M 129 | 66,F 130 | 55,M 131 | 49,M 132 | 36,M 133 | 46,M 134 | 55,F 135 | 40,M 136 | 56,F 137 | 68,F 138 | 71,F 139 | 57,M 140 | 57,F 141 | 33,F 142 | 53,F 143 | 81,M 144 | 19,F 145 | 51,M 146 | 41,M 147 | 47,F 148 | 50,F 149 | 50,F 150 | 76,F 151 | 84,F 152 | 36,F 153 | 79,M 154 | 48,M 155 | 44,F 156 | 29,M 157 | 43,F 158 | 45,F 159 | 41,F 160 | 64,M 161 | 71,M 162 | 52,M 163 | 45,F 164 | 47,F 165 | 74,F 166 | 73,M 167 | 59,F 168 | 53,F 169 | 62,F 170 | 60,M 171 | 37,F 172 | 55,F 173 | 55,F 174 | 53,M 175 | 73,F 176 | 36,F 177 | 48,F 178 | 58,F 179 | 42,M 180 | 57,F 181 | 45,M 182 | 79,F 183 | 17,F 184 | 25,F 185 | 35,F 186 | 23,F 187 | 61,F 188 | 59,M 189 | 76,M 190 | 77,M 191 | 75,F 192 | 71,F 193 | 78,F 194 | 88,M 195 | 46,F 196 | 58,F 197 | 46,F 198 | 55,M 199 | 58,F 200 | 42,F 201 | 69,M 202 | 69,M 203 | 43,F 204 | 56,F 205 | 62,F 206 | 67,M 207 | 56,M 208 | 52,F 209 | 43,F 210 | 75,F 211 | 62,F 212 | 44,F 213 | 38,F 214 | 74,M 215 | 35,F 216 | 37,F 217 | 61,F 218 | 68,F 219 | 77,F 220 | 63,M 221 | 35,M 222 | 41,F 223 | 44,F 224 | 45,F 225 | 26,F 226 | 51,F 227 | 56,F 228 | 24,F 229 | 45,F 230 | 59,F 231 | 75,F 232 | 59,F 233 | 63,F 234 | 45,F 235 | 20,F 236 | 50,F 237 | 81,F 238 | 81,F 239 | 41,F 240 | 46,F 241 | 54,F 242 | 27,F 243 | 82,F 244 | 58,M 245 | 47,F 246 | 54,F 247 | 46,F 248 | 34,F 249 | 50,F 250 | 26,M 251 | 48,F 252 | 64,M 253 | 83,F 254 | 56,F 255 | 75,M 256 | 56,F 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802,0,0,0,0,0,0 805 | 803,0,0,0,0,0,0 806 | 804,0,0,0,0,0,0 807 | 805,0,0,0,0,0,0 808 | 806,0,0,0,0,0,0 809 | 807,0,0,0,0,0,0 810 | 808,0,0,0,0,0,0 811 | 809,0,0,0,0,0,0 812 | 810,0,0,0,0,0,0 813 | 811,0,0,0,0,0,0 814 | 812,0,0,0,0,0,0 815 | 813,0,0,0,0,0,0 816 | 814,0,0,0,0,0,0 817 | 815,0,0,0,0,0,0 818 | 816,0,0,0,0,0,0 819 | 817,0,0,0,0,0,0 820 | 818,0,0,0,0,0,0 821 | 819,0,0,0,0,0,0 822 | 820,0,0,0,0,0,0 823 | 821,0,0,0,0,0,0 824 | 822,0,0,0,0,0,0 825 | 823,0,0,0,0,0,0 826 | 824,0,0,0,0,0,0 827 | 825,0,0,0,0,0,0 828 | 826,1,0,0,0,0,0 829 | -------------------------------------------------------------------------------- /generate_figures_and_tables.py: -------------------------------------------------------------------------------- 1 | # %% Import packages 2 | import pandas as pd 3 | import numpy as np 4 | from sklearn.metrics import (confusion_matrix, 5 | precision_score, recall_score, f1_score, 6 | precision_recall_curve, average_precision_score) 7 | import matplotlib.pyplot as plt 8 | import seaborn as sns 9 | import xarray as xr 10 | from scipy.stats.distributions import chi2 11 | from itertools import combinations 12 | 13 | 14 | # %% Auxiliar functions 15 | def get_scores(y_true, y_pred, score_fun): 16 | nclasses = np.shape(y_true)[1] 17 | scores = [] 18 | for name, fun in score_fun.items(): 19 | scores += [[fun(y_true[:, k], y_pred[:, k]) for k in range(nclasses)]] 20 | return np.array(scores).T 21 | 22 | 23 | def specificity_score(y_true, y_pred): 24 | m = confusion_matrix(y_true, y_pred, labels=[0, 1]) 25 | spc = m[0, 0] * 1.0 / (m[0, 0] + m[0, 1]) 26 | return spc 27 | 28 | 29 | def get_optimal_precision_recall(y_true, y_score): 30 | """Find precision and recall values that maximize f1 score.""" 31 | n = np.shape(y_true)[1] 32 | opt_precision = [] 33 | opt_recall = [] 34 | opt_threshold = [] 35 | for k in range(n): 36 | # Get precision-recall curve 37 | precision, recall, threshold = precision_recall_curve(y_true[:, k], y_score[:, k]) 38 | # Compute f1 score for each point (use nan_to_num to avoid nans messing up the results) 39 | f1_score = np.nan_to_num(2 * precision * recall / (precision + recall)) 40 | # Select threshold that maximize f1 score 41 | index = np.argmax(f1_score) 42 | opt_precision.append(precision[index]) 43 | opt_recall.append(recall[index]) 44 | t = threshold[index-1] if index != 0 else threshold[0]-1e-10 45 | opt_threshold.append(t) 46 | return np.array(opt_precision), np.array(opt_recall), np.array(opt_threshold) 47 | 48 | def affer_results(y_true, y_pred): 49 | """Return true positives, false positives, true negatives, false negatives. 50 | 51 | Parameters 52 | ---------- 53 | y_true : ndarray 54 | True value 55 | y_pred : ndarray 56 | Predicted value 57 | 58 | Returns 59 | ------- 60 | tn, tp, fn, fp: ndarray 61 | Boolean matrices containing true negatives, true positives, false negatives and false positives. 62 | cm : ndarray 63 | Matrix containing: 0 - true negative, 1 - true positive, 64 | 2 - false negative, and 3 - false positive. 65 | """ 66 | 67 | # True negative 68 | tn = (y_true == y_pred) & (y_pred == 0) 69 | # True positive 70 | tp = (y_true == y_pred) & (y_pred == 1) 71 | # False positive 72 | fp = (y_true != y_pred) & (y_pred == 1) 73 | # False negative 74 | fn = (y_true != y_pred) & (y_pred == 0) 75 | 76 | # Generate matrix of "tp, fp, tn, fn" 77 | m, n = np.shape(y_true) 78 | cm = np.zeros((m, n), dtype=int) 79 | cm[tn] = 0 80 | cm[tp] = 1 81 | cm[fn] = 2 82 | cm[fp] = 3 83 | return tn, tp, fn, fp, cm 84 | 85 | 86 | # %% Constants 87 | score_fun = {'Precision': precision_score, 88 | 'Recall': recall_score, 'Specificity': specificity_score, 89 | 'F1 score': f1_score} 90 | diagnosis = ['1dAVb', 'RBBB', 'LBBB', 'SB', 'AF', 'ST'] 91 | nclasses = len(diagnosis) 92 | predictor_names = ['DNN', 'cardio.', 'emerg.', 'stud.'] 93 | 94 | # %% Read datasets 95 | # Get two annotators 96 | y_cardiologist1 = pd.read_csv('./data/annotations/cardiologist1.csv').values 97 | y_cardiologist2 = pd.read_csv('./data/annotations/cardiologist2.csv').values 98 | # Get true values 99 | y_true = pd.read_csv('./data/annotations/gold_standard.csv').values 100 | # Get residents and students performance 101 | y_cardio = pd.read_csv('./data/annotations/cardiology_residents.csv').values 102 | y_emerg = pd.read_csv('./data/annotations/emergency_residents.csv').values 103 | y_student = pd.read_csv('./data/annotations/medical_students.csv').values 104 | # get y_score for different models 105 | y_score_list = [np.load('./dnn_predicts/other_seeds/model_' + str(i+1) + '.npy') for i in range(10)] 106 | 107 | 108 | # %% Get average model model 109 | # Get micro average precision 110 | micro_avg_precision = [average_precision_score(y_true[:, :6], y_score[:, :6], average='micro') 111 | for y_score in y_score_list] 112 | # get ordered index 113 | index = np.argsort(micro_avg_precision) 114 | print('Micro average precision') 115 | print(np.array(micro_avg_precision)[index]) 116 | # get 6th best model (immediatly above median) out 10 different models 117 | k_dnn_best = index[5] 118 | y_score_best = y_score_list[k_dnn_best] 119 | # Get threshold that yield the best precision recall using "get_optimal_precision_recall" on validation set 120 | # (we rounded it up to three decimal cases to make it easier to read...) 121 | threshold = np.array([0.124, 0.07, 0.05, 0.278, 0.390, 0.174]) 122 | mask = y_score_best > threshold 123 | # Get neural network prediction 124 | # This data was also saved in './data/annotations/dnn.csv' 125 | y_neuralnet = np.zeros_like(y_score_best) 126 | y_neuralnet[mask] = 1 127 | y_neuralnet[mask] = 1 128 | 129 | 130 | # %% Generate table with scores for the average model (Table 2) 131 | scores_list = [] 132 | for y_pred in [y_neuralnet, y_cardio, y_emerg, y_student]: 133 | # Compute scores 134 | scores = get_scores(y_true, y_pred, score_fun) 135 | # Put them into a data frame 136 | scores_df = pd.DataFrame(scores, index=diagnosis, columns=score_fun.keys()) 137 | # Append 138 | scores_list.append(scores_df) 139 | # Concatenate dataframes 140 | scores_all_df = pd.concat(scores_list, axis=1, keys=['DNN', 'cardio.', 'emerg.', 'stud.']) 141 | # Change multiindex levels 142 | scores_all_df = scores_all_df.swaplevel(0, 1, axis=1) 143 | scores_all_df = scores_all_df.reindex(level=0, columns=score_fun.keys()) 144 | # Save results 145 | scores_all_df.to_excel("./outputs/tables/scores.xlsx", float_format='%.3f') 146 | scores_all_df.to_csv("./outputs/tables/scores.csv", float_format='%.3f') 147 | 148 | 149 | # %% Plot precision recall curves (Figure 2) 150 | for k, name in enumerate(diagnosis): 151 | precision_list = [] 152 | recall_list = [] 153 | threshold_list = [] 154 | average_precision_list = [] 155 | fig, ax = plt.subplots() 156 | lw = 2 157 | t = ['bo', 'rv', 'gs', 'kd'] 158 | for j, y_score in enumerate(y_score_list): 159 | # Get precision-recall curve 160 | precision, recall, threshold = precision_recall_curve(y_true[:, k], y_score[:, k]) 161 | recall[np.isnan(recall)] = 0 # change nans to 0 162 | precision[np.isnan(precision)] = 0 # change nans to 0 163 | # Plot if is the choosen option 164 | if j == k_dnn_best: 165 | ax.plot(recall, precision, color='blue', alpha=0.7) 166 | # Compute average precision 167 | average_precision = average_precision_score(y_true[:, k], y_score[:, k]) 168 | precision_list += [precision] 169 | recall_list += [recall] 170 | average_precision_list += [average_precision] 171 | threshold_list += [threshold] 172 | # Plot shaded region containing maximum and minimun from other executions 173 | recall_all = np.concatenate(recall_list) 174 | recall_all = np.sort(recall_all) # sort 175 | recall_all = np.unique(recall_all) # remove repeated entries 176 | recall_vec = [] 177 | precision_min = [] 178 | precision_max = [] 179 | for r in recall_all: 180 | p_max = [max(precision[recall == r]) for recall, precision in zip(recall_list, precision_list)] 181 | p_min = [min(precision[recall == r]) for recall, precision in zip(recall_list, precision_list)] 182 | recall_vec += [r, r] 183 | precision_min += [min(p_max), min(p_min)] 184 | precision_max += [max(p_max), max(p_min)] 185 | ax.plot(recall_vec, precision_min, color='blue', alpha=0.3) 186 | ax.plot(recall_vec, precision_max, color='blue', alpha=0.3) 187 | ax.fill_between(recall_vec, precision_min, precision_max, 188 | facecolor="blue", alpha=0.3) 189 | # Plot iso-f1 curves 190 | f_scores = np.linspace(0.1, 0.95, num=15) 191 | for f_score in f_scores: 192 | x = np.linspace(0.0000001, 1, 1000) 193 | y = f_score * x / (2 * x - f_score) 194 | ax.plot(x[y >= 0], y[y >= 0], color='gray', ls=':', lw=0.7, alpha=0.25) 195 | # Plot values in 196 | for npred in range(4): 197 | ax.plot(scores_list[npred]['Recall'][k], scores_list[npred]['Precision'][k], 198 | t[npred], label=predictor_names[npred]) 199 | plt.xticks(fontsize=16) 200 | plt.yticks(fontsize=16) 201 | ax.set_xlim([0.0, 1.0]) 202 | ax.set_ylim([0.0, 1.02]) 203 | if k in [3, 4, 5]: 204 | ax.set_xlabel('Recall (Sensitivity)', fontsize=17) 205 | if k in [0, 3]: 206 | ax.set_ylabel('Precision (PPV)', fontsize=17) 207 | # plt.title('Precision-Recall curve (' + name + ')') 208 | if k == 0: 209 | plt.legend(loc="lower left", fontsize=17) 210 | else: 211 | ax.legend().remove() 212 | plt.tight_layout() 213 | plt.savefig('./outputs/figures/precision_recall_{0}.pdf'.format(name)) 214 | 215 | # %% Confusion matrices (Supplementary Table 1) 216 | 217 | M = [[confusion_matrix(y_true[:, k], y_pred[:, k], labels=[0, 1]) 218 | for k in range(nclasses)] for y_pred in [y_neuralnet, y_cardio, y_emerg, y_student]] 219 | 220 | M_xarray = xr.DataArray(np.array(M), 221 | dims=['predictor', 'diagnosis', 'true label', 'predicted label'], 222 | coords={'predictor': ['DNN', 'cardio.', 'emerg.', 'stud.'], 223 | 'diagnosis': diagnosis, 224 | 'true label': ['not present', 'present'], 225 | 'predicted label': ['not present', 'present']}) 226 | confusion_matrices = M_xarray.to_dataframe('n') 227 | confusion_matrices = confusion_matrices.reorder_levels([1, 2, 3, 0], axis=0) 228 | confusion_matrices = confusion_matrices.unstack() 229 | confusion_matrices = confusion_matrices.unstack() 230 | confusion_matrices = confusion_matrices['n'] 231 | confusion_matrices.to_excel("./outputs/tables/confusion matrices.xlsx", float_format='%.3f') 232 | confusion_matrices.to_csv("./outputs/tables/confusion matrices.csv", float_format='%.3f') 233 | 234 | 235 | #%% Compute scores and bootstraped version of these scores 236 | 237 | bootstrap_nsamples = 1000 238 | percentiles = [2.5, 97.5] 239 | scores_resampled_list = [] 240 | scores_percentiles_list = [] 241 | for y_pred in [y_neuralnet, y_cardio, y_emerg, y_student]: 242 | # Compute bootstraped samples 243 | np.random.seed(123) # NEVER change this =P 244 | n, _ = np.shape(y_true) 245 | samples = np.random.randint(n, size=n * bootstrap_nsamples) 246 | # Get samples 247 | y_true_resampled = np.reshape(y_true[samples, :], (bootstrap_nsamples, n, nclasses)) 248 | y_doctors_resampled = np.reshape(y_pred[samples, :], (bootstrap_nsamples, n, nclasses)) 249 | # Apply functions 250 | scores_resampled = np.array([get_scores(y_true_resampled[i, :, :], y_doctors_resampled[i, :, :], score_fun) 251 | for i in range(bootstrap_nsamples)]) 252 | # Sort scores 253 | scores_resampled.sort(axis=0) 254 | # Append 255 | scores_resampled_list.append(scores_resampled) 256 | 257 | # Compute percentiles index 258 | i = [int(p / 100.0 * bootstrap_nsamples) for p in percentiles] 259 | # Get percentiles 260 | scores_percentiles = scores_resampled[i, :, :] 261 | # Convert percentiles to a dataframe 262 | scores_percentiles_df = pd.concat([pd.DataFrame(x, index=diagnosis, columns=score_fun.keys()) 263 | for x in scores_percentiles], keys=['p1', 'p2'], axis=1) 264 | # Change multiindex levels 265 | scores_percentiles_df = scores_percentiles_df.swaplevel(0, 1, axis=1) 266 | scores_percentiles_df = scores_percentiles_df.reindex(level=0, columns=score_fun.keys()) 267 | # Append 268 | scores_percentiles_list.append(scores_percentiles_df) 269 | # Concatenate dataframes 270 | scores_percentiles_all_df = pd.concat(scores_percentiles_list, axis=1, keys=predictor_names) 271 | # Change multiindex levels 272 | scores_percentiles_all_df = scores_percentiles_all_df.reorder_levels([1, 0, 2], axis=1) 273 | scores_percentiles_all_df = scores_percentiles_all_df.reindex(level=0, columns=score_fun.keys()) 274 | 275 | 276 | #%% Print box plot (Supplementary Figure 1) 277 | # Convert to xarray 278 | scores_resampled_xr = xr.DataArray(np.array(scores_resampled_list), 279 | dims=['predictor', 'n', 'diagnosis', 'score_fun'], 280 | coords={ 281 | 'predictor': predictor_names, 282 | 'n': range(bootstrap_nsamples), 283 | 'diagnosis': ['1dAVb', 'RBBB', 'LBBB', 'SB', 'AF', 'ST'], 284 | 'score_fun': list(score_fun.keys())}) 285 | # Remove everything except f1_score 286 | for sf in score_fun: 287 | fig, ax = plt.subplots() 288 | f1_score_resampled_xr = scores_resampled_xr.sel(score_fun=sf) 289 | # Convert to dataframe 290 | f1_score_resampled_df = f1_score_resampled_xr.to_dataframe(name=sf).reset_index(level=[0, 1, 2]) 291 | # Plot seaborn 292 | ax = sns.boxplot(x="diagnosis", y=sf, hue="predictor", data=f1_score_resampled_df) 293 | # Save results 294 | plt.xticks(fontsize=16) 295 | plt.yticks(fontsize=16) 296 | plt.xlabel("") 297 | plt.ylabel("", fontsize=16) 298 | if sf == "F1 score": 299 | plt.legend(fontsize=17) 300 | else: 301 | ax.legend().remove() 302 | plt.tight_layout() 303 | plt.savefig('./outputs/figures/boxplot_bootstrap_{}.pdf'.format(sf)) 304 | 305 | 306 | scores_resampled_xr.to_dataframe(name='score').to_csv('./outputs/figures/boxplot_bootstrap_data.txt') 307 | 308 | #%% McNemar test (Supplementary Table 3) 309 | # Get correct and wrong predictions for each of them (cm >= 2 correspond to wrong predictions) 310 | wrong_predictions = np.array([affer_results(y_true, y_pred)[4] >= 2 311 | for y_pred in [y_neuralnet, y_cardio, y_emerg, y_student]]) 312 | 313 | # Compute McNemar score 314 | names = ["DNN", "cardio.", "emerg.", "stud."] 315 | mcnemar_name = [] 316 | mcnemar_score = np.empty((6, 6)) 317 | k = 0 318 | for i in range(4): 319 | for j in range(i+1, 4): 320 | a_not_b = np.sum(wrong_predictions[i, :, :] & ~wrong_predictions[j, :, :], axis=0) 321 | b_not_a = np.sum(~wrong_predictions[i, :, :] & wrong_predictions[j, :, :], axis=0) 322 | # An alterantive to the standard McNemar test is to include a 323 | # continuity correction term, resulting in: 324 | # mcnemar_corr_score = np.square(np.abs(a_not_b - b_not_a) - 1) / (a_not_b + b_not_a) 325 | # I tested both and came the conclusion, that we cannot reject the null hypotesis 326 | # for neither. The standard test however provide results that are easier to visualize. 327 | mcnemar_score[k, :] = np.square(a_not_b - b_not_a) / (a_not_b + b_not_a) 328 | k += 1 329 | mcnemar_name += [names[i] + " vs " + names[j]] 330 | 331 | mcnemar = pd.DataFrame(1-chi2.cdf(mcnemar_score, 1), index=mcnemar_name, columns=diagnosis) # p-value 332 | 333 | # Save results 334 | mcnemar.to_excel("./outputs/tables/mcnemar.xlsx", float_format='%.3f') 335 | mcnemar.to_csv("./outputs/tables/mcnemar.csv", float_format='%.3f') 336 | 337 | # %% Kappa score classifiers (Supplementary Table 2(a)) 338 | 339 | names = ["DNN", "cardio.", "emerg.", "stud."] 340 | predictors = [y_neuralnet, y_cardio, y_emerg, y_student] 341 | kappa_name = [] 342 | kappa_score = np.empty((6, 6)) 343 | k = 0 344 | for i in range(4): 345 | for j in range(i+1, 4): 346 | y_pred_1 = predictors[i] 347 | y_pred_2 = predictors[j] 348 | # Get "confusion matrix" 349 | negative_negative, positive_positive, positive_negative, negative_positive, _ = \ 350 | affer_results(y_pred_1, y_pred_2) 351 | p_p = positive_positive.sum(axis=0) 352 | p_n = positive_negative.sum(axis=0) 353 | n_p = negative_positive.sum(axis=0) 354 | n_n = negative_negative.sum(axis=0) 355 | total_sum = p_p + p_n + n_p + n_n 356 | # Relative agreement 357 | r_agree = (p_p + n_n) / total_sum 358 | # Empirical probability of both saying yes 359 | p_yes = (p_p + p_n) * (p_p + n_p) / total_sum**2 360 | # Empirical probability of both saying no 361 | p_no = (n_n + n_p) * (n_n + p_n) / total_sum**2 362 | # Empirical probability of agreement 363 | p_agree = p_yes + p_no 364 | # Kappa score 365 | kappa_score[k, :] = (r_agree - p_agree) / (1 - p_agree) 366 | k += 1 367 | kappa_name += [names[i] + " vs " + names[j]] 368 | 369 | kappa = pd.DataFrame(kappa_score, index=kappa_name, columns=diagnosis) # p-value 370 | 371 | # Save results 372 | kappa.to_excel("./outputs/tables/kappa.xlsx", float_format='%.3f') 373 | kappa.to_csv("./outputs/tables/kappa.csv", float_format='%.3f') 374 | 375 | 376 | # %% Kappa score dataset generation (Supplementary Table 2(b)) 377 | 378 | # Compute kappa score 379 | kappa_list = [] 380 | names_list = [] 381 | raters = [('DNN', y_neuralnet), ('Cert. cardiol. 1', y_cardiologist1), ('Certif. cardiol. 2', y_cardiologist2)] 382 | for r1, r2 in combinations(raters, 2): 383 | name1, y1 = r1 384 | name2, y2 = r2 385 | negative_negative, positive_positive, positive_negative, negative_positive, _ = \ 386 | affer_results(y1, y2) 387 | p_p = positive_positive.sum(axis=0) 388 | p_n = positive_negative.sum(axis=0) 389 | n_p = negative_positive.sum(axis=0) 390 | n_n = negative_negative.sum(axis=0) 391 | total_sum = p_p + p_n + n_p + n_n 392 | # Relative agreement 393 | r_agree = (p_p + n_n) / total_sum 394 | # Empirical probability of both saying yes 395 | p_yes = (p_p + p_n) * (p_p + n_p) / total_sum ** 2 396 | # Empirical probability of both saying no 397 | p_no = (n_n + n_p) * (n_n + p_n) / total_sum ** 2 398 | # Empirical probability of agreement 399 | p_agree = p_yes + p_no 400 | # Kappa score 401 | kappa = (r_agree - p_agree) / (1 - p_agree) 402 | kappa_list.append(kappa) 403 | names_list.append('{} vs {}'.format(name1, name2)) 404 | 405 | kappas_annotators_and_DNN = pd.DataFrame(np.stack(kappa_list), columns=diagnosis, index=names_list) 406 | print(kappas_annotators_and_DNN) 407 | kappas_annotators_and_DNN.to_excel("./outputs/tables/kappas_annotators_and_DNN.xlsx", float_format='%.3f') 408 | kappas_annotators_and_DNN.to_csv("./outputs/tables/kappas_annotators_and_DNN.csv", float_format='%.3f') 409 | 410 | # %% Compute scores and bootstraped version of these scores on alternative splits 411 | bootstrap_nsamples = 1000 412 | scores_resampled_list = [] 413 | scores_percentiles_list = [] 414 | for name in ['normal_order', 'date_order', 'individual_patients', 'base_model']: 415 | print(name) 416 | # Get data 417 | yn_true = y_true 418 | yn_score = np.load('./dnn_predicts/other_splits/model_'+name+'.npy') if not name == 'base_model' else y_score_best 419 | # Compute threshold 420 | nclasses = np.shape(yn_true)[1] 421 | opt_precision, opt_recall, threshold = get_optimal_precision_recall(yn_true, yn_score) 422 | mask_n = yn_score > threshold 423 | yn_pred = np.zeros_like(yn_score) 424 | yn_pred[mask_n] = 1 425 | # Compute bootstraped samples 426 | np.random.seed(123) # NEVER change this =P 427 | n, _ = np.shape(yn_true) 428 | samples = np.random.randint(n, size=n * bootstrap_nsamples) 429 | # Get samples 430 | y_true_resampled = np.reshape(yn_true[samples, :], (bootstrap_nsamples, n, nclasses)) 431 | y_doctors_resampled = np.reshape(yn_pred[samples, :], (bootstrap_nsamples, n, nclasses)) 432 | # Apply functions 433 | scores_resampled = np.array([get_scores(y_true_resampled[i, :, :], y_doctors_resampled[i, :, :], score_fun) 434 | for i in range(bootstrap_nsamples)]) 435 | # Sort scores 436 | scores_resampled.sort(axis=0) 437 | # Append 438 | scores_resampled_list.append(scores_resampled) 439 | 440 | # Compute percentiles index 441 | i = [int(p / 100.0 * bootstrap_nsamples) for p in percentiles] 442 | # Get percentiles 443 | scores_percentiles = scores_resampled[i, :, :] 444 | # Convert percentiles to a dataframe 445 | scores_percentiles_df = pd.concat([pd.DataFrame(x, index=diagnosis, columns=score_fun.keys()) 446 | for x in scores_percentiles], keys=['p1', 'p2'], axis=1) 447 | # Change multiindex levels 448 | scores_percentiles_df = scores_percentiles_df.swaplevel(0, 1, axis=1) 449 | scores_percentiles_df = scores_percentiles_df.reindex(level=0, columns=score_fun.keys()) 450 | # Append 451 | scores_percentiles_list.append(scores_percentiles_df) 452 | 453 | # %% Print box plot on alternative splits (Supplementary Figure 2 (a)) 454 | scores_resampled_xr = xr.DataArray(np.array(scores_resampled_list), 455 | dims=['predictor', 'n', 'diagnosis', 'score_fun'], 456 | coords={ 457 | 'predictor': ['random', 'by date', 'by patient', 'original DNN'], 458 | 'n': range(bootstrap_nsamples), 459 | 'diagnosis': ['1dAVb', 'RBBB', 'LBBB', 'SB', 'AF', 'ST'], 460 | 'score_fun': list(score_fun.keys())}) 461 | # Remove everything except f1_score 462 | sf = 'F1 score' 463 | fig, ax = plt.subplots() 464 | f1_score_resampled_xr = scores_resampled_xr.sel(score_fun=sf) 465 | # Convert to dataframe 466 | f1_score_resampled_df = f1_score_resampled_xr.to_dataframe(name=sf).reset_index(level=[0, 1, 2]) 467 | # Plot seaborn 468 | ax = sns.boxplot(x="diagnosis", y=sf, hue="predictor", data=f1_score_resampled_df, 469 | order=['1dAVb', 'SB', 'AF', 'ST', 'RBBB', 'LBBB'], 470 | palette=sns.color_palette("Set1", n_colors=8)) 471 | plt.axvline(3.5, color='black', ls='--') 472 | plt.axvline(5.5, color='black', ls='--') 473 | plt.axvspan(3.5, 5.5, alpha=0.1, color='gray') 474 | # Save results 475 | plt.xticks(fontsize=16) 476 | plt.yticks(fontsize=16) 477 | plt.xlabel("") 478 | plt.ylabel("F1 score", fontsize=16) 479 | plt.legend(fontsize=17) 480 | plt.ylim([0.4, 1.05]) 481 | plt.xlim([-0.5, 5.5]) 482 | plt.tight_layout() 483 | plt.savefig('./outputs/figures/boxplot_bootstrap_other_splits_{0}.pdf'.format(sf)) 484 | f1_score_resampled_df.to_csv('./outputs/figures/boxplot_bootstrap_other_splits_data.txt', index=False) --------------------------------------------------------------------------------