LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients - Wenjie YU; Hongwen CHEN; Hongliang QI; Zhilin PAN; Hanwei LI; Debin HU (Zhongguo yiliao qixie zazhi)
ObjectiveThe prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition. MethodsUsing 8 patients' data as samples, the RR intervals of patients were predicted by long short-term memory network (LSTM) and gradient lift tree (XGBoost), and the prediction results of the two models were combined by the inverse variance method to overcome the disadvantage of single model prediction. ResultsCompared with the single model, the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients. ConclusionLSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients, which has potential clinical feasibility.