论文标题
基于经验模式分解和人工神经网络的肺声信号降噪的组合模型
A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network
论文作者
论文摘要
近年来，已经提出了对肺部声音（LS）信号的计算机分析，以分析肺部状态的工具，但始终存在主要挑战，包括对LS的LS污染具有环境噪音，这些污染来自不同的强度来源。 LS信号降噪的常见方法之一是基于离散小波变换（DWT）系数（DWT）系数或信号的经验模式分解（EMD）的阈值，但是，在这些方法中，有必要计算SNR值以确定噪声去除噪声的适当阈值。为了解决这个问题，在这项研究中提出了一个基于EMD和人工神经网络（ANN）的组合模型（ANN）（ANN）（ANN）（ANN）（0、5、10、15和20DB）。该模型可以将白色和粉红色的噪声置于-2至20dB范围内，而无需阈值甚至估计SNR，同时保持LS信号的主要内容。还将提出的方法与EMD-Custom方法进行了比较，并从SNR获得的结果，并拟合标准表明该方法的绝对优越性。例如，在SNR = 0dB处，合并的方法可以分别对白色和粉红色噪声提高9.41和8.23db，而EMD-Custom方法的相应值分别为5.89和4.31db。
Computer analysis of Lung Sound (LS) signals has been proposed in recent years as a tool to analyze the lungs' status but there have always been main challenges, including the contamination of LS with environmental noises, which come from different sources of unlike intensities. One of the common methods in noise reduction of LS signals is based on thresholding on Discrete Wavelet Transform (DWT) coefficients or Empirical Mode Decomposition (EMD) of the signal, however, in these methods, it is necessary to calculate the SNR value to determine the appropriate threshold for noise removal. To solve this problem, a combined model based on EMD and Artificial Neural Network (ANN) trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in this research. The model can denoise white and pink noises in the range of -2 to 20dB without thresholding or even estimating SNR, and at the same time, keep the main content of the LS signal well. The proposed method is also compared with the EMD-custom method, and the results obtained from the SNR, and fit criteria indicate the absolute superiority of the proposed method. For example, at SNR = 0dB, the combined method can improve the SNR by 9.41 and 8.23dB for white and pink noises, respectively, while the corresponding values are respectively 5.89 and 4.31dB for the EMD-Custom method.
