Lung sound signal denoising using discrete wavelet transform and artificial neural network

Abstract

Computerized analysis of Lung Sound (LS) is a promising method for assessing pulmonary function. However, the LS signal is severely contaminated by background noise from various sources. Conventional denoising methods may not be practical due to the noisy nature of the LS as well as its spectral overlap with different noise sources. This paper proposes an adaptive technique based on Discrete Wavelet Transform and Artificial Neural Network (DWT-ANN) to filtrate LS signals in a noisy environment. This new method mixes the multi-resolution property of DWT with ANN as a nonlinear adaptive filter. In this research, separate models for signal denoising with different SNRs (0, 5, 10, and 15 dB) were designed. Then a single model was introduced as a combined model to eliminate any information about input signals SNR before the denoising process. The results showed that the combined model, in addition to having a close performance to the individual models, could perform the denoising process well in the range of −2 to 20 dB, which is outside the range that the model has been trained with. In addition, comparing the results of our proposed method with the DWT method, it was observed that the SNR of the denoised signal was significantly enhanced. At SNR = 0 dB, this improvement is 9.18 compared to only 3.90 using the DWT method.