Yu Fang, Zhiyuan Huang, Hongxia Leng, Liu Dongbo, Weibo Wang
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China.
Phys Eng Sci Med. 2025 Mar;48(1):207-220. doi: 10.1007/s13246-024-01506-w. Epub 2025 Jan 30.
Hypertrophic cardiomyopathy (HCM), including obstructive HCM and non-obstructive HCM, can lead to sudden cardiac arrest in adolescents and athletes. Early diagnosis and treatment through auscultation of different types of HCM can prevent the occurrence of malignant events. However, it is challenging to distinguish the pathological information of HCM related to differential left ventricular outflow tract pressure gradients. To address this issue, a classification method based on weighted bispectrum features of heart sounds (HSs) is proposed for efficient and cost-effective HCM analysis. Preprocessing is first applied to remove background noise during HS acquisition. Then, the bispectrum contour map is calculated, and 56-dimensional features are extracted to represent the pathological information of HCM. Next, an adaptive threshold weighting mutual information method is proposed for feature selection and weighted fusion. Finally, the CNN-RF classifier model is built to automatically identify different types of HCM cases. A clinical dataset of normal and two types of HCM HSs is utilized for validation. The results show that the proposed method performs well, with a classification accuracy reaching 94.4%. It provides a reliable reference for HCM diagnosis in young patients in clinical settings.
肥厚型心肌病（HCM），包括梗阻性HCM和非梗阻性HCM，可导致青少年和运动员心脏骤停。通过听诊不同类型的HCM进行早期诊断和治疗可预防恶性事件的发生。然而，区分与左心室流出道压力梯度差异相关的HCM病理信息具有挑战性。为了解决这个问题，提出了一种基于心音（HS）加权双谱特征的分类方法，用于高效且经济高效的HCM分析。首先进行预处理以去除HS采集期间的背景噪声。然后，计算双谱等高线图，并提取56维特征以表示HCM的病理信息。接下来，提出一种自适应阈值加权互信息方法用于特征选择和加权融合。最后，构建CNN-RF分类器模型以自动识别不同类型的HCM病例。利用正常和两种类型HCM HS的临床数据集进行验证。结果表明，所提出的方法性能良好，分类准确率达到94.4%。它为临床环境中年轻患者的HCM诊断提供了可靠的参考。
