Classification of adventitious respiratory sounds through histogram of oriented gradients
DOI:
https://doi.org/10.46842/ipn.cien.v28n1a04Keywords:
Histogram of oriented gradients, adventitious respiratory sounds, short time Fourier transform, continuous wavelet transformAbstract
Respiratory diseases represent one of the leading causes of death in the world. Prevention, timely diagnosis and effective treatment are fundamental pillars to reduce the spread of diseases as well as their negative impact on society. The present work is focused on developing a computational model, through the use of the Histogram of Oriented Gradients (HOG) algorithm in conjunction with machine learning techniques, capable of classifying adventitious respiratory sounds; to support the timely diagnosis of respiratory diseases. For this purpose, it is proposed to use the HOG as a feature extractor, which has not been explored in the current literature on the classification of respiratory sounds. Similarly, it is proposed to use different machine learning algorithms, such as: Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forests (RF). In addition, evaluate the model according to the framework provided by the ICBHI17 database. The main contribution of the present work is the analysis of different configurations of the histogram of oriented gradients for optimizing machine learning models. Where, after a series of experiments, our best model emerged with the Alternative 4 (ALT 4) configuration; which obtained as results: 55.07% ACC, 34.37% BAL, 51.52% SCC, 75.87% SPE and 27.18% SEN.
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