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

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。

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 invasive 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 obtained by using the fusion filter bank.Finally,the obtained fusion feature parameters are input into the TabPFN network for classification 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.