A pulse rate prediction method in artifacts segments based on Elman neural network

In this study, the pulse rate (PR) of the artifact segments is predicted using a neural network. Predicting pulse rate be-comes a complicated problem due to the impaired collection of arterial blood pressure (ABP) signals. We propose to build a time series of interference pulse rate and discuss the best prediction model based on an Elman neural network under fixed and asynchronous length conditions. Meanwhile, the effectiveness and accuracy of the proposed method are validated when compared to the sliding window prediction method. The minimum RMSE, MAPE, and MAE of the proposed method were 0.0292, 1.9742%, and 0.0176. At the same time, this research laid the groundwork for discovering cardiovascular diseases.