Lozano-Garcia Manuel, Nuhic Jasna, Moxham John, Rafferty Gerrard F, Jolley Caroline J, Jane Raimon
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2740-2743. doi: 10.1109/EMBC44109.2020.9176215.
Lung sound (LS) signals are often contaminated by impulsive artifacts that complicate the estimation of lung sound intensity (LSI) using conventional amplitude estimators. Fixed sample entropy (fSampEn) has proven to be robust to cardiac artifacts in myographic respiratory signals. Similarly, fSampEn is expected to be robust to artifacts in LS signals, thus providing accurate LSI estimates. However, the choice of fSampEn parameters depends on the application and fSampEn has not previously been applied to LS signals. This study aimed to perform an evaluation of the performance of the most relevant fSampEn parameters on LS signals, and to propose optimal fSampEn parameters for LSI estimation. Different combinations of fSampEn parameters were analyzed in LS signals recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing. The performance of fSampEn was assessed by means of its cross-covariance with flow signals, and optimal fSampEn parameters for LSI estimation were proposed.
肺音（LS）信号常常受到脉冲伪迹的干扰，这使得使用传统幅度估计器来估计肺音强度（LSI）变得复杂。固定样本熵（fSampEn）已被证明对肌电图呼吸信号中的心脏伪迹具有鲁棒性。同样，预计fSampEn对LS信号中的伪迹也具有鲁棒性，从而能够提供准确的LSI估计。然而，fSampEn参数的选择取决于应用，并且此前fSampEn尚未应用于LS信号。本研究旨在评估最相关的fSampEn参数对LS信号的性能，并提出用于LSI估计的最佳fSampEn参数。在健康受试者和慢性阻塞性肺疾病患者的异质群体在负荷呼吸期间记录的LS信号中，分析了fSampEn参数的不同组合。通过fSampEn与流量信号的互协方差来评估其性能，并提出用于LSI估计的最佳fSampEn参数。
