Self-supervised learning is used to extract features from raw PPG signals and initializing end-to-end deep learning models to predict hospitalization given raw PPG signals. Extracted features are used to predict hospitalization using logistic regression. We also relate extracted features to known physiological parameters such as respiratory rate, heart rate and oxygen saturation (SpO2) using linear regression. Details about the analysis can be found in this manuscript.

The analysis is done using pytorch==1.7.1 (updated to 1.12.1 in revised analysis), pytorch-metric-learning==0.9.96, and hyper-parameter optimization using ray[tune]==1.2.0. Bayesian models are fitted using pyro-ppl==1.8.2. Install packages in requirements.txt

• Install packages in requirements.txt
• Create directories for data and results as contained in setting.py
• Prepare data by runing datasets/segments.py. The datasets can be requested from https://doi.org/10.7910/DVN/KQ4DNK, https://doi.org/10.5683/SP2/PHX4C5, and https://doi.org/10.5683/SP2/ZDDFZL

Self-supervised learning model are trained using contrastive learning (models/contrastive_resnet.py) using Noise Contrastive Estimation loss and dot product as the distance metric.

Features extracted using contrastive learning are used as predictors of heart rate, respiratory rate and oxygen saturation (SpO2) in models/regression.py and models/regression_bayesian.py.

Classification of hospitalization using features extracted using self-supervised learning: models/admission_classifier.py and models/logistic_bayesian.py

Classification of hospitalization using end-to-end deep learning:models/end_to_end.py

End to end models can either be initialized randomly or using weights of the self-supervised model