基于LMS-PNN算法在心音识别与预测中的应用

传统的概率神经网络（Probability neural network, PNN）具有很强的容错性、学习过程简单、训练速度快等特点。为提高传统PNN在心音分类方面的性能，利用最小均方(Least mean square, LMS)方法对其进行优化，进而提高心音分类与预测的准确性。LMS-PNN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方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，抽取40 000个样本数据进行训练，并对各心音进行等级划分与预测。从PNN的模式层输入训练数据后，由实验数据验证可知，LMS-PNN算法的预测准确率可达96%以上。

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

Traditional probability neural network (PNN) has strong fault tolerance, simple learning process and fast training speed. To improve the performance of the traditional PNN in heart sound classification, we adopt least mean square (LMS) method to implement the optimization, thereby increasing the accuracy of heart sound classification and prediction. The LMS-PNN algorithm frames the heart sound signal using the window function, uses the double threshold method to determine the value of the data, employs the LMS algorithm to debug the corresponding parameters, and saves the denoised data in the format of mat file. It extracts the short-time autocorrelation coefficients and short- time power spectral densities of each heart sound, and uses PNN to extract 40 000 sample data for training. Each heart sound is graded and predicted. After inputting the training data from the mode layer of the PNN algorithm, experimental data verification shows that the prediction accuracy of LMS-PNN can reach more than 96%.