Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.
胎儿心电信号提取对围产期胎儿监护具备重要意义。为提高胎儿心电信号的预测精度，本文提出一种基于遗传算法（GA）优化的长短时记忆（LSTM）网络胎儿心电信号提取方法（GA-LSTM）。首先根据母体腹壁混合心电信号的特征，利用 GA 的全局搜索能力对 LSTM 网络中隐层神经元个数、学习率和训练次数进行寻优，计算参数的最优组合，使网络拓扑结构与母体腹壁混合信号的特征相匹配；然后，使用 GA 求出的最优网络参数构建 LSTM 网络模型，并利用 GA-LSTM 网络模型估计母体胸部心电信号传输到母体腹壁时的非线性变换；最后，利用母体胸部心电信号和 GA-LSTM 网络模型求得的非线性变换，估计腹壁信号中所含的母体心电信号，从腹壁混合信号中减去估计出的母体心电信号，得到纯净的胎儿心电信号。本文实验应用两个数据库的临床心电信号进行实验分析，最终结果表明：与传统归一化最小均方误差（NLMS）方法、支持向量机（SVM）方法、遗传算法支持向量机（GA-SVM）方法和 LSTM 网络方法相比，本文所提出的方法可以提取出更为清晰的胎儿心电信号，其准确率、灵敏度、精确性和总体概率均有较好的提高，表明本文方法可以提取出较为纯净的胎儿心电信号，对围产期胎儿健康监护具有一定的应用价值。.
Keywords: fetal electrocardiogram signals; genetic algorithm; long short term memory network; nonlinear estimation.