School of Electronics and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China.
Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou 313000, People's Republic of China.
Biomed Phys Eng Express. 2023 Mar 1;9(3). doi: 10.1088/2057-1976/acbc7f.
Cardiopulmonary auscultation is promising to get smart due to the emerging of electronic stethoscopes. Cardiac and lung sounds often appear mixed at both time and frequency domain, hence deteriorating the auscultation quality and the further diagnosis performance. The conventional cardiopulmonary sound separation methods may be challenged by the diversity in cardiac/lung sounds. In this study, the data-driven feature learning advantage of deep autoencoder and the common quasi-cyclostationarity characteristic are exploited for monaural separation.Different from most of the existing separation methods that only handle the amplitude of short-time Fourier transform (STFT) spectrum, a complex-valued U-net (CUnet) with deep autoencoder structure, is built to fully exploit both the amplitude and phase information. As a common characteristic of cardiopulmonary sounds, quasi-cyclostationarity of cardiac sound is involved in the loss function for training.. In experiments to separate cardiac/lung sounds for heart valve disorder auscultation, the averaged achieved signal distortion ratio (SDR), signal interference ratio (SIR), and signal artifact ratio (SAR) in cardiac sounds are 7.84 dB, 21.72 dB, and 8.06 dB, respectively. The detection accuracy of aortic stenosis can be raised from 92.21% to 97.90%.. The proposed method can promote the cardiopulmonary sound separation performance, and may improve the detection accuracy for cardiopulmonary diseases.
心肺听诊由于电子听诊器的出现而变得智能化。心音和肺音在时域和频域上常常混合在一起，因此会降低听诊质量和进一步的诊断性能。传统的心肺音分离方法可能会受到心音/肺音多样性的挑战。在这项研究中，深度自动编码器的数据驱动特征学习优势和常见的准循环平稳特性被用于单声道分离。与大多数仅处理短时傅里叶变换（STFT）谱幅度的现有分离方法不同，构建了具有深度自动编码器结构的复值 U 型网络（CUnet），以充分利用幅度和相位信息。心音的准循环平稳性是心肺音的共同特征，被纳入训练的损失函数中。在心瓣膜疾病听诊中用于分离心音/肺音的实验中，心音的平均获得信号失真比（SDR）、信号干扰比（SIR）和信号伪影比（SAR）分别为 7.84dB、21.72dB 和 8.06dB。主动脉瓣狭窄的检测准确率可以从 92.21%提高到 97.90%。该方法可以提高心肺音分离性能，并可能提高心肺疾病的检测准确率。
