【摘要】 针对传统心音分类方法大多选择单通道信号的一维特征，可能会丢失不同通道、周期之间的病理关联性的问题，提出了一种基于多导心音信号提取3D矩阵特征进行心脏病分类的方法。首先对各通道的心音信号应用巴特沃斯滤波器去噪，接着定位心电R波峰值完成心音分割，并从中提取包括Welch法功率谱能量在内的15个时频域有效特征。其次将这15个时频特征按通道数×周期数×特征数的方式堆叠成一个3D矩阵特征集，并将此3D矩阵特征作为卷积神经网络(CNN)分类器的输入进行心音分类。该方法对测试数据集的126例正常心音和185例异常心音进行分类，准确率可达到98.9%;对临床采集的4种共126例先心病心音及正常心音进行细化分类，分类准确率可达93.9%。实验结果表明，3D矩阵特征能够有效地提取心音信号中的病理特征，相比于单通道特征，分类准确率提高了2.7%,可为心脏病临床治疗提供辅助参考。更多还原

【Abstract】 This study proposes a method for classifying cardiac diseases by extracting 3D matrix features from multi-channel heart sound signals, addressing the limitations of traditional methods that primarily utilize one-dimensional features from single-channel signals, which may overlook pathological correlations across different channels and cycles. First, a Butterworth filter was applied for noise reduction on the heart sound signals from each channel. The R-wave peaks were then located to segment the heart sounds, from which 15 effective time-frequency features, including Welch method power spectral energy, are extracted. Subsequently, these features were stacked into a 3D matrix with dimensions corresponding to the number of channels, cycles, and features, with the optimal cycle number determined to be 4. This 3D matrix was directly used as input for a CNN classifier. The method was tested on a dataset comprising 126 normal and 185 abnormal heart sounds, achieving an accuracy of 98.9%. Additionally, the method was validated on 126 clinical cases of congenital heart disease sounds and normal sounds, resulting in a classification accuracy of 93.9%. These experimental results indicated that the 3D matrix features could effectively capture pathological characteristics in the heart sound signals, improving the classification accuracy by 2.7% compared to single-channel features, providing valuable assistance for clinical cardiac treatment.更多还原