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Description
Human auscultation represents an easy and prompt tool for physicians to diagnose rapidly the existence of a pulmonary disease. A revolutionary tool called “stethoscope” was introduced by Rene Laennec in 1816 and gradually refined ever since. However, the usage of the stethoscope still heavily relies on empirical knowledge. As a result, the process of new physician training in acquiring pulmonary auscultation skills is still time consuming. Moreover, the success rate as revealed by the training examination is also unsatisfactorily elusive. To avoid the subjective nature of clinical pulmonary auscultation, efforts have been carried out to digitally record and analyze lung sounds and further explore the correlation between the acoustic characteristics with the clinical symptoms. In our project, we propose to find the correlation between the acoustic signal generation and the tracheal internal geometry. Instead of carrying out experiments on human beings, we chose to use a 3Dprinted model based on geometry data collected from a real human patient. Our main purpose is to understand how the geometry of the trachea wall interacts with the air flow and thus, impacts the acoustic signal generated and acquired by the microphone. Once we understand the pattern for a healthy trachea, we will alter its internal geometry to simulate a tracheal disease. For this, we will investigate a simplified model of stenosis by simply create a small obstruction inside the trachea. The microphone provides a flat frequency response between 50 and 3000 Hz. This range is commensurate with typical frequency of tracheal breath sound which varies from 100 to 1,500 Hz. To carry out our experimental measurements, we use an artificial air pump to simulate the respiratory process in the trachea. The 3D-printed trachea is placed in an anechoic box as shown in the figure below. The acoustic signal is amplified using a signal amplifier, low-pass filtered at 3000 Hz, and recorded by a digital data acquisition system. The signal is then analyzed using spectral analysis methods to establish the correlation between the tracheal acoustic signal and its internal geometry.
