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.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):977-985. doi: 10.7507/1001-5515.202310016.
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.
心血管疾病（CVD）是全球主要死因之一。心音分类在心血管疾病的早期检测中起着关键作用。正常心音与异常心音之间的差异并不明显。本文为提高心音分类模型的准确性，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）相结合用于心音分类。该模型利用双谱分析可有效抑制高斯噪声，且无需依赖心音信号的精确分割就能有效提取心音信号特征。同时，该模型结合卷积神经网络强大的分类性能，最终实现心音的准确分类。根据实验结果，在相同数据和实验条件下，所提算法在准确率、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，所提算法有显著提升，具有很强的鲁棒性和泛化能力，有望应用于先天性心脏病的辅助检测。
