Classification of heart sounds based on quality assessment and wavelet scattering transform
Classification of heart sounds based on quality assessment and wavelet scattering transform
复制标题
基于质量评估和小波散射变换的心音分类
DOI：
10.1016/j.compbiomed.2021.104814
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发表时间：
2021-09-01
影响因子：
7.7
通讯作者：
Wei, Shoushui
中科院分区：
文献类型：
--
作者：
Mei, Na;Wang, Hongxia;Wei, Shoushui
心音自动分类在心血管疾病的诊断中具有重要作用。提出了一种基于质量评价和小波散射变换的心音样本分类方法。首先，采用过零比（RZC）和逐次差均方根（RMSSD）对心音信号质量进行评价。选取符合阈值标准的第一个信号段作为连续心音信号的电流样本。利用小波散射变换，将小波散射系数按小波尺度维度展开，得到特征。采用支持向量机（SVM）进行分类，并采用小波尺度维数投票法对样本进行分类，得到分类结果。详细讨论了RZC和RMSSD对结果的影响。在2016年心脏病学挑战赛（cinc2016）的PhysioNet计算数据库中，该方法的准确率为92.23% (Acc)，灵敏度为96.62% (Se)，特异性为90.65% (Sp)，准确度为93.64% （Macc）。结果表明，该方法能有效地对正常和异常心音样本进行分类，准确率较高。
Automatic classification of heart sound plays an important role in the diagnosis of cardiovascular diseases. In this study, a heart sound sample classification method based on quality assessment and wavelet scattering transform was proposed. First, the ratio of zero crossings (RZC) and the root mean square of successive differences (RMSSD) were used for assessing the quality of heart sound signal. The first signal segment conforming to the threshold standard was selected as the current sample for the continuous heart sound signal. Using the wavelet scattering transform, the wavelet scattering coefficients were expanded according to the wavelet scale dimension, to obtain the features. Support vector machine (SVM) was used for classification, and the classification results for the samples were obtained using the wavelet scale dimension voting approach. The effects of RZC and RMSSD on the results are discussed in detail. On the database of PhysioNet Computing in Cardiology Challenge 2016 (CinC 2016), the proposed method yields 92.23% accuracy (Acc), 96.62% sensitivity (Se), 90.65% specificity (Sp), and 93.64% measure of accuracy (Macc). The results show that the proposed method can effectively classify normal and abnormal heart sound samples with high accuracy.