摘要
针对目前心音分析算法识别率不够高的问题,提出了一种基于变值逻辑模型的先心病心音分类算法.首先,对心音信号进行预处理,去除非病理性噪声并提取心音包络;然后,对心音信号的包络数据进行变值逻辑运算,对包络数据进行标记将其转换为伪DNA序列,并转换为可视化分析的测度数据;最后,使用Inception_Resnet_v2等深度学习模型对常见先心病心音信号进行多分类测试,并与其它已有的算法进行对比.研究所用的正常和异常心音样本共1000例,其中在测试集上进行多分类的平均准确率为0.931.实验结果表明,该算法优于目前已有的几种算法,有望用于先心病初诊的机器辅助诊断.
Due to the problem that the accuracy of the existing algorithms was not good enough,a novel method of the classification of congenital heart disease(CHD)heart sound based on deep learning models with the variable logic theory model was put forward in this paper.Firstly,the heart sound signal was pre-processed to remove non-pathological noise and extract the heart sound envelope.Secondly,the variable logic theory was applied to the feature extraction,labeled the envelope data of the heart sound signal and converted into Pseudo-DNA sequence and visual analysis measurement data.Finally,the deep learning models,such as Inception_Resnet_v2 was used to perform multi-classification for some common CHD heart sounds and compared with other existing algorithms.There were 1000 cases of heart sound used in this study.The average accuracies of 0.931 for multi-classification of heart sound were obtained on test set by using the novel method.The experimental results showed that the algorithm performs better than the several existing algorithms.It is expected to be used for machine-assisted diagnosis of the initial diagnosis of congenital heart disease.
作者
姚如苹
孙静
潘家华
王威廉
YAO Ru-ping;SUN Jing;PAN Jia-hua;WANG Wei-lian(School of Information Science&Engineering,Yunnan University,Kunming 650500,Yunnan,China;Fuwai Yunnan Cardiovascular Hospital,Kunming 650032,Yunnan,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第5期859-867,共9页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金(81960067)
云南省科技厅重大科技专项(2018ZF017).
关键词
变值逻辑模型
先心病
心音信号
特征提取
深度学习
variable logic model
congenital heart disease(CHD)
heart sound signal
feature extraction
deep learning
