Clinical grade SpO2 prediction through semi-supervised learning

We reuse photoplethysmography (PPG) datasets obtained during the clinical testing of a wrist-worn reflectance pulse oximeter and build a deep neural network (DNN) with the goal of achieving RMSE <; 3.5% for SpO2 levels ranging from 70% to 100%, which is the required clinical accuracy. We show that, with supervised learning alone, the DNN does not achieve the required performance (RMSE = 4.4%). When pretraining the DNN in an unsupervised manner with a method based on contrastive representation learning to take advantage of a set of unlabelled PPG signals, the DNN achieves clinical-grade accuracy (RMSE = 2.91%). This work therefore highlights the importance of semi-supervised learning for the development of wearable medical devices, for which unlabelled data is abundant but labelled data is scarce.