基于Poincare Plot和LSTM的胎心率分类算法

Classification of Fetal Heart Rate Based on Poincare Plot and LSTM

胎心率在母胎监护和胎儿健康检测中起着非常重要的作用。该研究对胎心率信号进行研究,提出了一种基于庞加莱图(Poincare Plot),和长短期记忆网络(LSTM)的胎心率分类算法,实现胎心率异常情况的高性能分类。首先采用插值法对捷克技术大学布尔诺大学医院(Czech Technical University-University Hospital in Brno,CTU-UHB)数据库的原始胎心率信号进行降噪预处理。然后将时序的胎心率信号转换为庞加莱图,得到信号的非线性特征。再利用SquenzeNet对庞加莱图进行特征提取,最后用LSTM对SqueezeNet提取出的特征数据进行分类。经2000个胎心率样本的测试,得到准确率、真正例率和反正例率分别为98.00%、100.00%、92.30%,相较于传统胎心率分类算法,各方面参数均有提高。该研究提出的方法在CTU-UHB胎监数据库上具有良好的性能,对辅助胎心率检测的临床诊断具有一定的实际价值。

Fetal heart rate plays an essential role in maternal and fetal monitoring and fetal health detection. Inthis study, a method based on Poincare Plot and LSTM is proposed to realize the high performanceclassification of abnormal fetal heart rate. Firstly, the original fetal heart rate signal of CTU-UHB databaseis preprocessed via interpolation, then the sequential fetal heart rate signal is converted into Poincare Plotto obtain nonlinear characteristics of the signals, and then SquenzeNet is used to extract the features ofPoincare Plot. Finally, the features extracted by SqueezeNet are classified by LSTM. And the accuracy ,the true positive rate and the false positive rate are 98.00%, 100.00%, 92.30% respectively on 2 000 testset data. Compared with the traditional fetal heart rate classification method, all respects are improved.The method proposed in this study has good performance in CTU-UHB fetal monitoring database andhas certain practical value in the clinical diagnosis of auxiliary fetal heart rate detection.