Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases. To reach accurate diagnoses, a device must be able to recognize heart and lung sounds from various clinical situations. However, the recorded chest sounds are mixed by heart and lung sounds. Thus, effectively separating these two sounds is critical in the pre-processing stage. Recent advances in machine learning have progressed on monaural source separations, but most of the well-known techniques require paired mixed sounds and individual pure sounds for model training. As the preparation of pure heart and lung sounds is difficult, special designs must be considered to derive effective heart and lung sound separation techniques. In this study, we proposed a novel periodicity-coded deep auto-encoder (PC-DAE) approach to separate mixed heart-lung sounds in an unsupervised manner via the assumption of different periodicities between heart rate and respiration rate. The PC-DAE benefits from deep-learning-based models by extracting representative features and considers the periodicity of heart and lung sounds to carry out the separation. We evaluated PC-DAE on two datasets. The first one includes sounds from the Student Auscultation Manikin (SAM), and the second is prepared by recording chest sounds in real-world conditions. Experimental results indicate that PC-DAE outperforms several well-known separation works in terms of standardized evaluation metrics. Moreover, waveforms and spectrograms demonstrate the effectiveness of PC-DAE compared to existing approaches. It is also confirmed that by using the proposed PC-DAE as a pre-processing stage, the heart sound recognition accuracies can be notably boosted. The experimental results confirmed the effectiveness of PC-DAE and its potential to be used in clinical applications.

中文翻译：

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基于周期编码深度自编码器的心肺盲单耳声源分离。

听诊是诊断心血管和呼吸系统疾病最有效的方法。为了达到准确的诊断，设备必须能够识别来自各种临床情况的心肺声音。然而，记录的胸音是由心音和肺音混合而成的。因此，有效地分离这两种声音在预处理阶段至关重要。机器学习的最新进展在单声道声源分离方面取得了进展，但大多数众所周知的技术需要成对的混合声音和单独的纯声音进行模型训练。由于制备纯心肺音难度较大，必须考虑特殊设计，才能推导出有效的心肺音分离技术。在这项研究中，我们提出了一种新的周期性编码深度自动编码器 (PC-DAE) 方法，通过假设心率和呼吸率之间存在不同的周期性，以无监督的方式分离混合的心肺音。PC-DAE 受益于基于深度学习的模型，通过提取代表性特征并考虑心肺音的周期性来进行分离。我们在两个数据集上评估了 PC-DAE。第一个包括来自学生听诊模型 (SAM) 的声音，第二个是通过在现实条件下记录胸部声音来准备的。实验结果表明，PC-DAE 在标准化评估指标方面优于几个众所周知的分离工作。此外，波形和频谱图证明了 PC-DAE 与现有方法相比的有效性。还证实，通过使用所提出的 PC-DAE 作为预处理阶段，可以显着提高心音识别精度。实验结果证实了 PC-DAE 的有效性及其在临床应用中的潜力。