On the effectiveness of signal decomposition, feature extraction and selection on lung sound classification

TL;DR

This study evaluates various signal decomposition and feature extraction techniques for classifying lung sounds, demonstrating that combining spectral features with feature selection improves accuracy and reduces input complexity.

Contribution

It systematically compares decomposition and feature extraction methods for lung sound classification, highlighting effective combinations and the benefits of feature selection.

Findings

Higher-order spectral features improve classification accuracy.

Feature selection reduces input features without losing accuracy.

kNN classifier achieved the best performance.

Abstract

Lung sounds refer to the sound generated by air moving through the respiratory system. These sounds, as most biomedical signals, are non-linear and non-stationary. A vital part of using the lung sound for disease detection is discrimination between normal lung sound and abnormal lung sound. In this paper, several approaches for classifying between no-crackle and crackle lung sounds are explored. Decomposition methods such as Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, and Discrete Wavelet Transform are used along with several feature extraction techniques like Principal Component Analysis and Autoencoder, to explore how various classifiers perform for the given task. An open-source dataset downloaded from Kaggle, containing chest auscultation of varying quality is used to determine the results of using the different decomposition and feature extraction combinations. It is found that when higher-order statistical and spectral features along with the Mel-frequency cepstral coefficients are fed to the classier we get the best performance with the kNN classifier giving the best accuracy. Furthermore, it is also demonstrated that using a combination of feature selection methods one can significantly reduce the number of input features without adversely affecting the accuracy of the classifiers.