基于LMS-PNN神经网络算法在心音识别与预测中的应用

传统的PNN神经网络具有很强的容错性、学习过程简单、训练速度快等特点，本文在传统PNN神经网络的基础上，利用LMS对其在心音分类方面进行优化，进而提高心音分类与预测的准确性。LMS-PNN神经网络算法对心音的信号运用窗函数进行分帧，利用双门限法确定数据的值，运用LMS算法对相应的参数进行调试，并将去噪后的数据以mat格式保存，提取出各个心音的短时自相关系数以及短时功率谱密度，并运用PNN神经网络，抽取40000个样本数据进行训练，并将各个心音进行等级划分与预测。 从PNN神经网络的模式层输入训练数据后，通过仿真测试可得，LMS—PNN神经网络预测准确率可达可达96%以上。

Application of LMS-PNN Neural Network Algorithm in Heart Sound Recognition and Prediction

The traditional PNN neural network has strong fault tolerance, simple learning process and fast training speed. Based on the traditional PNN neural network, this paper uses LMS to optimize its heart sound classification, and then improve heart sound classification and prediction. accuracy. The LMS-PNN neural network algorithm framing the heart sound signal using the window function, using the double threshold method to determine the value of the data, using the LMS algorithm to debug the corresponding parameters, and saving the denoised data in mat format, extracting The short-time autocorrelation coefficients and short-term power spectral densities of each heart sound are used, and PNN neural network is used to extract 40,000 sample data for training, and each heart sound is graded and predicted. After inputting the training data from the mode layer of the PNN neural network, it can be obtained through simulation test that the prediction accuracy of the LMS-PNN neural network can reach more than 96%.