We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy.

Block diagram of heart sound classification.

我们旨在提出一种新颖的方法，该方法结合了改进的频率切片小波变换（MFSWT）和卷积神经网络（CNN）来对正常和异常心音进行分类。隐藏的马尔可夫模型用于在心音信号中查找每个心动周期的位置，并确定S1，S2，心脏收缩和舒张四个周期的确切位置。然后，使用MFSWT将一维心动周期信号转换为二维时频图像。最后，使用上述图片训练了两个CNN模型。我们使用样本熵（SampEn）组合了两个CNN模型，以确定哪个模型用于对心音信号进行分类。我们根据PhysioNet Computing in Cardiology Challenge 2016提供的心音公共数据集评估了我们的模型。来自10倍交叉验证的实验分类性能表明，灵敏度（Se），特异性（Sp）和平均准确度（MAcc）分别为0.95、0.93和0.94。结果表明，所提方法能够有效，准确地对正常和异常心音进行分类。

心音分类的框图。