摘要
在全球的死亡案例中,心血管疾病(CVD)是主要的致死原因之一。心音分类识别在心血管疾病的早期发现中起着关键作用。正常心音和异常心音之间的区别并不明显,本文为提升心音分类模型的准确度,提出一种基于双谱分析的心音特征提取方法,并将其与卷积神经网络(CNN)结合,对心音进行分类。该算法能够有效地利用双谱分析来抑制高斯噪声,而且不需要准确分割心音信号就能提取其特征,同时结合了卷积神经网络的强大分类性能,从而实现对心音的准确分类。根据实验结果显示,在相同的数据和实验条件下,本文提出的算法在准确率、灵敏度和特异性方面分别达到了0.910、0.884和0.940。与其他心音分类算法相比,本文算法提升明显,并具有较强的鲁棒性和泛化能力,因此有望应用于先心病的辅助检测。
Cardiovascular disease(CVD)is one of the leading causes of death worldwide.Heart sound classification plays a key role in the early detection of CVD.The difference between normal and abnormal heart sounds is not obvious.In this paper,in order to improve the accuracy of the heart sound classification model,we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network(CNN)to classify heart sounds.The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals.At the same time,the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound.According to the experimental results,the proposed algorithm achieves 0.910,0.884 and 0.940 in terms of accuracy,sensitivity and specificity under the same data and experimental conditions,respectively.Compared with other heart sound classification algorithms,the proposed algorithm shows a significant improvement and strong robustness and generalization ability,so it is expected to be applied to the auxiliary detection of congenital heart disease.
作者
彭利勇
全海燕
PENG Liyong;QUAN Haiyan(Department of Communication Engineering,Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
北大核心
2024年第5期977-985,994,共10页
Journal of Biomedical Engineering
基金
国家自然科学基金项目(61861023)。
关键词
心血管疾病
双谱分析
心音分类
卷积神经网络
Cardiovascular disease
Bispectral analysis
Heart sound classification
Convolutional neural network
