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Communication Dans Un Congrès Année : 2020

Clinical grade SpO2 prediction through semi-supervised learning

Résumé

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 \lt 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. © 2020 IEEE.
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Dates et versions

hal-03129852 , version 1 (03-02-2021)

Identifiants

Citer

G. Priem, C. Martinez, Q. Bodinier, G. Carrault. Clinical grade SpO2 prediction through semi-supervised learning. 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020, Oct 2020, Cincinnati, United States. pp.914-921, ⟨10.1109/BIBE50027.2020.00155⟩. ⟨hal-03129852⟩
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