  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 separations 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.
