Chest X-rays and computed tomography scans are highly accurate lung assessment tools, but their hazardous nature and high cost remain a barrier for many patients. Acoustic imaging is an alternative to lung function assessment that is non-hazardous, less costly, and has a patient-to-equipment approach. In this thesis, the suitability of acoustic imaging for lung health assessment is proven via systematic review and numerical airway modelling. An acoustic lung sound acquisition system, consisting of an optimal denoising filter translated into imaging for continual and reliable lung function assessment, is then developed.
To the author’s best knowledge, locating obstructed airways via an acoustic lung model andthe resulting acoustic lung imaging have yet to be investigated in the open literature; hence,a novel acoustic lung spatial model was first developed in this research, which links acousticlung sounds and acoustic images with pathologic changes. About 89% structural similaritybetween an acoustic reference image based on actual lung sound and the developed modelacoustic image based on the computation of airway impedance was achieved.
External interference is inevitable in lung sound recordings; thus, an indirect unifying of wavelet-based total variation (WATV) and empirical Wiener denoising filter is proposed to enhance recorded lung sound signals. To the author’s best knowledge, the integration of WATV and Wiener filters has not been investigated for lung sound signals. Selection and analysis of optimal parameters for the denoising filter were performed through a case study. The optimal parameters achieved through simulation studies led to an average 12.69 ± 5.05 dB improvement in signal-to-noise ratio (SNR), and the average SNR was improved by 16.92 ± 8.51 dB in the experimental studies. The hybrid denoising filter significantly enhances the signal quality of the captured lung sounds while preserving the characteristics of a lung sound signal and is less sensitive to the variation of SNR values of the input signal.
A robust system was developed based on the established lung spatial model and denoising filter through hardware redesign and signal processing, which outperformed commercial digital stethoscopes regarding SNR and root mean square error by about 8 dB and 0.15, respectively. Regarding sensing sensitivity power spectrum mapping, the developed system sensors’ position is neutral, as opposed to digital stethoscopes, when representing lung signals, with a signal power loss ratio of around 5 dB compared to 10 dB from digital stethoscopes. The developed system obtains better detection by about 10% in the obstructed airway region compared to digital stethoscopes in the experimental studies
