摘要
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IS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。
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%.
作者
周克良
王佳佳
Zhou Keliang;Wang Jiajia(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou,341000,China)
出处
《数据采集与处理》
CSCD
北大核心
2019年第5期831-836,共6页
Journal of Data Acquisition and Processing
基金
国家自然科学基金（61363011）资助项目
江西省自然科学基金（20151BAB207024）资助项目
关键词
心音
最小均方(LMS)
短时自相关系数
短时功率谱密度
概率神经网络(PNN)
heart sound
least mean square(LMS)
short-time autocorrelation coefficient
short-time power spectral density
probability neural network(PNN)
