Comput. Biol. Med. - Wavelet-based denoising method for real phonocardiography signal recorded by mobile devices in noisy environment.

Tópicos

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Resumo

The main obstacle in development of intelligent autodiagnosis medical systems based on the analysis of phonocardiography (PCG) signals is noise. The noise can be caused by digestive and respiration sounds, movements or even signals from the surrounding environment and it is characterized by wide frequency and intensity spectrum. This spectrum overlaps the heart tones spectrum, which makes the problem of PCG signal filtrating complex. The most common method for filtering such signals are wavelet denoising algorithms. In previous studies, in order to determine the optimum wavelet denoising parameters the disturbances were simulated by Gaussian white noise. However, this paper shows that this noise has a variable character. Therefore, the purpose of this paper is adaptation of a wavelet denoising algorithm for the filtration of real PCG signal disturbances from signals recorded by a mobile devices in a noisy environment. The best results were obtained for Coif 5 wavelet at the 10th decomposition level with the use of a minimaxi threshold selection algorithm and mln rescaling function. The performance of the algorithm was tested on four pathological heart sounds: early systolic murmur, ejection click, late systolic murmur and pansystolic murmur.

Resumo Limpo

main obstacl develop intellig autodiagnosi medic system base analysi phonocardiographi pcg signal nois nois can caus digest respir sound movement even signal surround environ character wide frequenc intens spectrum spectrum overlap heart tone spectrum make problem pcg signal filtrat complex common method filter signal wavelet denois algorithm previous studi order determin optimum wavelet denois paramet disturb simul gaussian white nois howev paper show nois variabl charact therefor purpos paper adapt wavelet denois algorithm filtrat real pcg signal disturb signal record mobil devic noisi environ best result obtain coif wavelet th decomposit level use minimaxi threshold select algorithm mln rescal function perform algorithm test four patholog heart sound earli systol murmur eject click late systol murmur pansystol murmur

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