一种基于随机森林和BP神经网络的心室颤动识别方法

A Ventricular Fibrillation Recognition Method Based on Random Forest and BP Neural Network

心室颤动是导致心搏骤停最常见的病理生理机制,心搏骤停若能得到及时救助,就能大幅度提高患者存活率,因此,快速准确识别心室颤动极为重要。该研究提出一种基于BP(back propagation)神经网络和随机森林的心室颤动自动检测算法。将心电信号通过6 s的移动窗口,根据信号的时频域信息,计算出6种特征参数,将这6种特征参数作为分类器的输入,进行分类和测试,并以数据库中权威专家给定的标签作为参考输出,共使用了44例相关数据对该方法进行了评估。十折交叉验证法结果表明,该方法在CU数据库(Creighton University Ventricular Tachyarrhythmia Database)和AHA数据库(The American Heart Association Database)中心室颤动分类准确率达到了96.38%和99.45%,具有一定的可应用性。

Ventricular fibrillation is the most common pathophysiological mechanism leading to cardiac arrest.If cardiac arrest can be rescued in time,the survival rate of patients can be greatly improved.Therefore,rapid and accurate identification of ventricular fibrillation is extremely important.This paper proposes an automatic detection algorithm for ventricular fibrillation based on random forest and BP(back propagation) neural network.Pass the ECG signal through a 6 s moving window,calculate 6 kinds of characteristic parameters according to the time-frequency domain information of the signal,use these 6 kinds of characteristic parameters as the input of the classifier,carry out classification and test,and give the authoritative experts in the database.A total of 44 cases of related data were used to evaluate the method.The results show that using the ten-fold cross-validation method,the accuracy of classification of ventricular fibrillation in the CU database(Creighton University Ventricular Tachyarrhythmia Database) and the AHA database(the American Heart Association Database) has reached 96.38% and 99.45%,which has certain applicability.