Wang Zichao, Jin Yanrui, Zhao Liqun, Liu Chengliang
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R.China.
Department of Cardiology, Shanghai First People's Hospital, Shanghai 200080, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Feb 25;38(1):10-20. doi: 10.7507/1001-5515.202006025.
Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.
心音是诊断心血管疾病的常见医学信号之一。本文研究正常与异常心音之间的二分类问题，提出一种基于极端梯度提升（XGBoost）和深度神经网络联合决策的心音分类算法，在特征提取和模型准确性方面取得了进一步提升。首先，将预处理后的心音记录分割为四种状态，并基于分割从信号中提取五类特征。前四类特征通过递归特征消除进行筛选，用作XGBoost分类器的输入。最后一类是梅尔频率倒谱系数（MFCC），用作长短期记忆网络（LSTM）的输入。考虑到数据集的不平衡性，对这两个分类器都进行了加权改进。最后，采用异构集成决策方法获得预测结果。该算法应用于PhysioNet网站上2016年PhysioNet计算心脏病学（CINC）挑战赛的公开心音数据库，以测试灵敏度、特异性、修正准确率和F分数。结果分别为93%、89.4%、91.2%和91.3%。与其他研究人员使用的机器学习、卷积神经网络（CNN）等方法的结果相比，准确率和灵敏度有了明显提高，证明本文方法能够有效提高心音信号分类的准确率，在某些心血管疾病的临床辅助诊断应用中具有很大潜力。
