Automated classification of human lung sound signals using phase space representation of intrinsic mode function

Abstract

Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

“…However, this method failed to reduce the computational overheads. Sibghatullah I. Khan et al [7] modeled an empirical mode decomposition (EMD) method for extracting the intrinsic mode functions (IMFs) of lung sounds because of its non-linear and non-stationary nature. However, this method failed to consider novel deep learning classifiers for improving the performance of classification.…”

A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE

“…When studies were analyzed, it was discovered that lung sounds were classified and substantial results were discovered [23,24,25]. The time-frequency domain features of normal and abnormal sounds have been the focus of studies analyzing normal and abnormal noises [26].…”

“…In numerous research, Mel frequency cepstral coefficients (MFCC) and estimated entropy have been used [27]. Additionally, recent research using empirical mode decomposition (EMD) and intrinsic mode functions (IMFs) demonstrate these methods' superior efficacy in classifying lung sounds as normal-adventive [23,25].…”