LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients

VernacularTitle:基于长短期记忆网络和梯度提升的高血压患者RR间期时间序列预测方法
Author: Wenjie YU ^{1
,

2} ; Hongwen CHEN ; Hongliang QI ; Zhilin PAN ; Hanwei LI ; Debin HU
Author Information

1. 南方医科大学生物医学工程学院,广州市,510515
2. 南方医科大学南方医院,广州市,510515
Keywords: RR intervals; long short-term memory network; gradient lift tree; time series forecasting; hypertension
From: Chinese Journal of Medical Instrumentation 2024;48(4):392-395
CountryChina
Language:Chinese
Abstract: Objective The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients'heart condition.Methods Using 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.Results Compared with the single model,the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients.Conclusion LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients,which has potential clinical feasibility.