Prediction of blood oxygen saturation based on deep learning

Yuchun Yang

doi:10.1117/12.2636385

6 May 2022 Prediction of blood oxygen saturation based on deep learning

Yuchun Yang

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217602 (2022) https://doi.org/10.1117/12.2636385
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

According to the different physiological indexes of medical patients, aiming at the data of blood oxygen saturation level in continuous monitoring period, combined with the booming trend of artificial intelligence and its potential in predicting blood oxygen saturation, this paper quantitatively analyzes the prediction results of different physiological indexes on blood oxygen saturation. First of all, in order not to lose useful information, all the characteristics of patients are taken as the characteristics of the input model. Secondly, in order to find out the internal relationship between different patients' oxygen saturation, we add the previous patients' oxygen saturation data to the input features. Finally, multiple regression model, RNN model, GRU model and LSTM model were established to predict the mean value of blood oxygen saturation. The results show that the deep learning model has faster convergence speed in the training set and better prediction effect in the test set. Compared with other models, LSTM model has the best prediction effect, RMSE = 0.021, MAPE = 0.017%, R2 = 0.99.

Citation Download Citation

Yuchun Yang "Prediction of blood oxygen saturation based on deep learning", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217602 (6 May 2022); https://doi.org/10.1117/12.2636385

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