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

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

摘要:
目的对高血压患者的RR间期进行预测，帮助临床医生对患者心脏状况进行分析和预警。
方法以8位患者数据为样本，通过长短期记忆网络（LSTM）和梯度提升树（XGBoost）分别对患者的RR间期进行预测，将2个模型的预测结果通过方差倒数法进行组合，克服单一模型预测的劣势。
结果提出的组合模型相较于单一模型在8位患者RR间期的预测上具有不同程度的改善效果。
结论 LSTM-XGBoost模型为高血压患者RR间期预测提供了方法，具有一定的临床价值。

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.