Automatic classification of heart sounds plays an important role in the early diagnosis of congenital heart disease. A kind of heart sound classification algorithms based on sub-band envelope feature and convolution neural network was proposed in this paper, which did not need to segment the heart sounds according to cardiac cycle accurately. Firstly, the heart sound signal was divided into some frames. Then, the frame level heart sound signal was filtered with Gammatone filter bank to obtain the sub-band signals. Next, the sub-band envelope was extracted by Hilbert transform. After that, the sub-band envelope was stacked into a feature map. Finally, type Ⅰ and type Ⅱ convolution neural network were selected as classifier. The result shown that the sub-band envelope feature was better in type Ⅰ than type Ⅱ. The algorithm is tested with 1 000 heart sound samples. The test results show that the overall performance of the algorithm proposed in this paper is significantly improved compared with other similar algorithms, which provides a new method for automatic classification of congenital heart disease, and speeds up the process of automatic classification of heart sounds applied to the actual screening.
心音自动分类技术在先天性心脏病的早期诊断中占有重要地位。本文在不依赖对心音按心动周期进行准确分割的基础上，提出一种基于子带包络特征和卷积神经网络的心音分类算法。首先对心音信号进行分帧，其次用伽马通滤波器组对帧级心音信号进行滤波从而得到子带信号，然后用希尔伯特变换提取子带包络并将经过后续处理的子带包络堆叠成特征图，最后使用Ⅰ型与Ⅱ型卷积神经网络进行分类，经实验证明该特征在Ⅰ型卷积神经网络上能达到较优效果。本文用采集的1 000例心音样本对本文算法进行测试，测试结果表明，本文提出的算法对比其它同类算法的整体性能有明显提升，期望通过本研究可为先心病的自动分类提供新的方法，并加快心音自动分类技术应用于实际筛查的进程。.
Keywords: Hilbert transform; convolution neural network; gammatone filter bank; heart sound classification; sub-band envelope.