Investigation of Lung Sounds Features for Detection of Bronchitis and COPD Using Machine Learning Methods

Abstract

The study is dedicated to the issue of investigation of the lung sounds digital analysis processing methods, searching for new informative features of pathological respiratory sounds and using machine learning methods for classifying the state of the bronchopulmonary system. In particular, the use of various methods of lung sounds analysis is considered, namely: frequency, time-frequency, wavelet, and mel-frequency cepstral analysis. The application of signal processing methods to the problem of respiration signals analysis is considered in the paper. In order to investigate the possibilities of machine learning to the problem of classification of respiration signals, the dataset of lung sounds of 296 recordings, which represent 3 classes: norm, bronchitis, and chronic obstructive pulmonary disease, was used in this work. The purpose of this study is to identify and compare the informative features of the lung sounds obtained with different signal processing methods, as well as to choose the classification method that provides the highest accuracy in the identification of the bronchopulmonary system condition. To obtain frequency features, power spectrum density dependence on frequency was calculated for respiratory signals using fast Fourier transform method. The spectral measures, as well as ratios of spectrum powers in different frequency bands, were defined. To extract the spectral-temporal features of the lung sounds, the spectrograms of the analyzed signals were investigated. The mean time dependences of the power spectral density in the indicated frequency ranges were determined. The sum of magnitudes values of the power spectrum curve for each frequency band was used as the features obtained from the spectrogram. The ratios of the energies corresponding to detail levels of the wavelet decomposition to the total energy of the decomposed signal were used as the parameters of signal recognition based on wavelet analysis. The logarithmic (mel) filterbank energies, averaged over time frames, depending on channel index and time, as well as mel frequency cepstrum depending on cepstrum index and time, are proposed to use as features derived from mel-cepstral analysis. The supervised machine learning based on decision trees, discriminant analysis, support vector machines, logistic regression, k-nearest neighbors classifiers, and ensemble learning were applied to determine the best classification models for computerized disease screening. The accuracy of the different classifiers using these feature sets was determined and compared. Based on this, a combination of features and classifiers, which provides the highest accuracy of lung condition recognition, reaching 93%, is proposed.