AIME 2020

This repository relates to section 3.2 from our paper:

HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population (Link)

Best student paper at the AIME 2020 conference.

For the code related to secion 3.3 please refer to this repository: PPG-to-BP-Prediction-convnets

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For running the notebooks:

pipenv install
pipenv shell
pipenv run jupyter notebook

Please add to your config.json file the paths to each dataset, e. g.:

"hype":"../../datasets/hype", # path to the hype dataset folder
"eval":"../../datasets/eval", # path to the processed eval dataset file referenced below

The notebooks process raw PPG data and predict blood pressure for 2 different datasets:

HYPE: extract_features_and_predict_bp_from_ppg_hype.ipynb

EVAL: extract_features_and_predict_bp_from_ppg_eval.ipynb

Datasets Information

EVAL: https://www.kaggle.com/mkachuee/noninvasivebp (original)

However, we processed it and used the following file as the input to our notebook: https://doi.org/10.6084/m9.figshare.12649691

HYPE: Available to the scientific community through a data agreement. Please fill in the following form: https://forms.gle/M8DDtuMeWGfT3k4y5

For generating the input for section 3.3 the above data needs to be processed using processing_hype_for_3_3/json_ppg_bp_window_hype.ipynb

Results Reproducibility

HYPE Dataset

Since it is not possible to reproduce the results exactly as in the paper (because not all subjects wanted to donate data to the broader research community), we re-executed the scripts with the available dataset tha can be obtained after filling in the form above. This should be the output of the notebook extract_features_and_predict_bp_from_ppg_hype.ipynb best_results:

predicted_variable	experiment_type	k	MAE_GBM_MEAN	MAE_GBM_STD	MAE_LGBM_MEAN	MAE_LGBM_STD	MAE_RF_MEAN	MAE_RF_STD	MAE_LR_MEAN	MAE_LR_STD	MAE_DUMMY_MEAN	MAE_DUMMY_STD
DBP	biking	2	7.613349978333324	3.106239967730272	7.360168155954208	2.117581898310436	8.061667542016806	3.3161004994965704	6.521003366875357	2.5584947009158774	7.506030701754385	2.05878506653711
DBP	24 Hours	2	11.0344698407823	2.445440419148344	10.744885158613748	2.243951294097582	10.790160681378364	2.109163817347919	12.093416128392777	3.038191731934865	11.806660613038815	3.5723128979166106
SBP	biking	2	9.879023447928953	1.662372428004632	9.513003095975233	0.7133487048013492	9.461666666666668	1.384995487357271	9.689095624747068	1.734356132739077	9.513003095975233	0.7133487048013492
SBP	24 Hours	2	14.760112750212937	4.268165375156772	14.869205297071844	4.351786287295664	15.468070025013558	4.078999099212703	15.664020493627312	4.623100877961526	15.435089876989352	4.161358479573512

EVAL Dataset

When processing this file: https://doi.org/10.6084/m9.figshare.12649691 using extract_features_and_predict_bp_from_ppg_eval.ipynb the best_results should be:

predicted_variable	experiment_type	k	MAE_GBM_MEAN	MAE_GBM_STD	MAE_LGBM_MEAN	MAE_LGBM_STD	MAE_RF_MEAN	MAE_RF_STD	MAE_LR_MEAN	MAE_LR_STD	MAE_DUMMY_MEAN	MAE_DUMMY_STD
DBP	kaggle	2	7.865446544017316	2.267393274853124	7.57220318998406	2.3845234314216945	7.644171242199798	2.489483420588713	7.871258403903799	2.0669919349077728	7.664836489656983	2.184583329953216
SBP	kaggle	2	16.18703094573625	4.355064754562451	16.005655885115857	4.625746788262742	16.745896699769194	4.784200038452515	16.707034825286065	4.32198051974363	15.54764126683809	4.9577070660961455

More Information

Morassi Sasso, Ariane (2020): https://figshare.com/projects/AIME_2020/85166