Respiratory sound denoising using Empirical Mode Decomposition, Hurst analysis and Spectral Subtraction

Abstract

Chronic pulmonary diseases, specifically Chronic Obstructive Pulmonary Disease (COPD), is in third position for causing deaths all over the globe. Misdiagnosis and higher health care cost is the reason behind the heavy loss of life every year. To detect such diseases, computerized respiratory sound based diagnosis is one of the non-invasive, economical, convenient and harmless procedures, which could be one of the solutions to this acute problem. But, this diagnostic method is often affected due to noise issues. This paper presents a new method to denoise the respiratory sound using empirical mode decomposition (EMD), Hurst analysis and spectral subtraction. Using this algorithm, the highest signal to noise ratio (SNR) acquired is 27.32 dB and the peak signal to noise ratio (PSNR) is 43.23 dB. The proposed denoising algorithm could be a significant approach for assisting clinicians to make clear interpretations from the respiratory sound. The future work will be based on the elimination of heart sound noises from the respiratory sound signal.