ELM and K-nn machine learning in classification of breath sounds signals

Abstract

The acquisition of Breath sounds (BS) signals from a human respiratory system with an electronic stethoscope, provide and offer prominent information which helps the doctors to diagnosis and classification of pulmonary diseases. Unfortunately, this BS signals with other biological signals have a non-stationary nature according to the variation of the lung volume, and this nature makes it difficult to analyze and classify between several diseases. In this study, we were focused on comparing the ability of the extreme learning machine (ELM) and k-nearest neighbour (K-nn) machine learning algorithms in the classification of adventitious and normal breath sounds. To do so, the empirical mode decomposition (EMD) was used in this work to analyze BS, this method is rarely used in the breath sounds analysis. After the EMD decomposition of the signals into Intrinsic Mode Functions (IMFs), the Hjorth descriptors (Activity) and Permutation Entropy (PE) features were extracted from each IMFs and combined for classification stage. The study has found that the combination of features (activity and PE) yielded an accuracy of 90.71%, 95% using ELM and K-nn respectively in binary classification (normal and abnormal breath sounds), and 83.57%, 86.42% in multiclass classification (five classes).

Lung disease recognition methods using audio-based analysis with machine learning

“…Most existing projects for lung disease diagnosis using respiratory sounds follow a three-stage machine learning pipeline: 1) the preprocessing, which involves removing unwanted noise and preparing the sound data for further analysis using audio filtering and noise-reduction techniques, 2) the feature extraction, which involves extracting relevant characteristics from the preprocessed sound data using signal processing methods like spectral analysis [ [39] , [40] , [41] , [42] ], cepstral analysis [ [43] , [44] , [45] ], wavelet transforms [ [46] , [47] , [48] ], and statistical analysis [ 49 ], 3) the classification, which uses extracted features to categorize the sounds as belonging to different disease categories. Popular classifiers include K-nearest Neighbors [ [50] , [51] , [52] , [53] , [54] ], Support Vector Machines [ [55] , [56] , [57] , [58] , [59] ], Gaussian Mixture models [ 60 , 61 ], and Artificial Neural Networks [ 36 , 55 ]. Fig.…”

Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases

“…The new object is assigned to the class K that has the shortest distance to class K which is defined as the nearest neighbor. 28 Ensemble classifiers mix results from many weak learners into one high-quality ensemble model. The SVM algorithm represents the training data as points in a flat separated space by an apparent gap.…”

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

“…The second phase is feature extraction, which is accomplished by the use of signal processing methods such as spectrum analysis [35][36][37][38], Cepstrum analysis [39][40][41], wavelet transformations [18,42,43], and statistics [44]. The third stage is classification, and the most often used classifiers were K-nearest Neighbors [32,[45][46][47][48], Support Vector Machines [49][50][51][52][53], Gaussian Mixture models [54,55], and Artificial Neural Network (ANN) [49,56]. The workflow representation from preprocessing to classification can be shown in Figure 6.…”