基于时频融合特征的肺动脉高压心音分类模型
Classification Model of Heart Sounds in Pulmonary Hypertension Based on Time-Frequency Fusion Features
王彦麟 1孙静 1杨宏波 2郭涛 2潘家华 2王威廉1
作者信息
- 1. 云南大学信息学院 昆明 650500
- 2. 昆明医科大学附属心血管病医院 昆明 650102
- 折叠
摘要
先心病相关肺动脉高压是一种严重的心血管疾病,致死率高,对其进行早期筛查与识别对于治愈尤为重要.目前临床是通过右心导管术确诊,此为有创检查,不便于在大规模筛查中采用,研究一种无创便捷的识别方法迫在眉睫.文中建立了一种时频融合的心音分类模型.首先对心音信号进行预处理,然后使用融合滤波器组对信号进行转换并求取动态时频特征,最后将得到的融合特征参数输入表格式先验数据拟合网络(TabPFN)中进行分类识别.实验结果表明,该算法在正常、CHD-PAH和CHD中的平均准确率、精确率、灵敏度、特异度和F1分别为92.21％,92.15％,92.15％,96.11％,92.14％.对于先心病相关肺动脉高压的早期筛查与识别具有重要意义.
Abstract
Pulmonary hypertension associated with congenital heart disease has a high mortality rate,and early screening and identification of it is particularly important for cure.At present,diagnosis is made by right heart catheterization,which is an inva-sive examination,it is not easy to use in screening,and has high risk and high cost.Therefore,it is urgent to study a non-invasive and convenient method for identification.In this paper,a time-frequency fusion heart sound classification model is established.First,the heart sound signal is preprocessed,then the signal is converted,and the dynamic time-frequency characteristics are ob-tained by using the fusion filter bank.Finally,the obtained fusion feature parameters are input into the TabPFN network for clas-sification and recognition.Experimental results indicate that the algorithm has average accuracy,precision,sensitivity,specificity,and F1 scores of 92.21％,92.15％,92.15％,96.11％,and 92.14％respectively in normal,CHD-PAH,and CHD.It is important for the early screening and identification of pulmonary hypertension associated with congenital heart disease.
关键词
心音/先心病相关肺动脉高压/动态特征提取/时频特征融合/表格式先验数据拟合网络Key words
Heart sound/Congenital heart disease-associated pulmonary arterial hypertension/Dynamic feature extraction/Time-Frequency feature fusion/Tabular prior-data fitted network(TabPFN)引用本文复制引用
基金项目
国家自然科学基金(81960067)
云南省科技重大专项(2018ZF017)
出版年
2024
NETL
NSTL
万方数据