Yuriy Georgievich Gorshkov - Ph.D.(Eng.), Associate Professor, Department of Information Security, Post-Doctorate Fellow, Department of Medical-Engineering Information Technologies (BMT-2), Bauman Moscow State Technical University, E-mail: y.gorshkov@npo-echelon.ru
Vladimir Borisovich Parashin - Dr.Sci.(Eng.), Professor

Computerized lung sound analysis involves recording the patient-s lung sounds via an electronic device, followed by computer analysis and classification of lung sounds based on specific signal characteristics. Auscultation typically takes place in a clinic setting where there could be multiple sources of ambient noise. Acoustic auscultation, however, is generally limited by poor signal transmission due to noise, tubular resonance effects, and greater attenuation of higher frequency sounds. This is an important factor to consider in pulmonary auscultation because lung sounds are mostly characterized in the higher frequency spectrum ranging from 3-5 Hz to 3000 Hz. On the other hand, electronic auscultation has the advantage of signal amplification and ambient noise reduction leading to increased signal-to-noise ratio along with its independence on human ear sensitivity to different acoustic frequencies. Rapid advancement in electronics and computer technology in recent years has increased research interest in automated classification of lung sounds among pulmonary researchers and has the potential to reduce software and hardware costs. Computerized lung sound analysis is a powerful tool for optimizing and quantifying electronic auscultation information based on the specific lung sound spectral characteristics. The Fourier transform is the most commonly used spectral analysis algorithm to provide information on the frequency components of a signal. Computer analysis of lung sounds is performed, usually based on Fourier transform, have disadvantages at processing non-stationary signals. Process the 40 learning lung sounds of the two manuals «Auscultation of the lungs» with high-precision algorithms of wavelet analysis. The examples obtained acoustic sonograms and frequency-time characteristics of lung sounds at various diseases. It is shown that acoustic sonograms multilevel wavelet analysis have higher frequency-time resolution than the Fourier spectrograms.

 

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