Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation

Abstract

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.Clinical Relevance-LSI is considered to be an indirect measurement of the airflow entering the lungs, so that LSI can be used to detect airway obstruction and assess lung disorders that affect ventilation. The clinical potential of LSI analysis depends, however, on the ability to obtain accurate estimates of LSI. The fSampEn parameters proposed in this study allow fSampEn to be optimally applied to LS signals, thus contributing to improving LSI estimation in health and disease.