Adventitious breath sounds in infant1/16/2024 ![]() Elhilali, Computerized lung sound screening for pediatric auscultation in noisy field environments, IEEE Trans. Olmez, Classification of respiratory sounds by using an artificial neural network, Int. Dokur, Respiratory sound classification by using an incremental supervised neural network, Pattern Anal. Bajaj, Convolutional neural networks based efficient approach for classification of lung diseases, Health Inf. Sengur, Classification of lung sounds with cnn model using parallel pooling structure, IEEE Access 8 ( 2020) 105376–105383. Landsberg, Analysis and automatic classification of breath sounds, IEEE Trans. Li, Triple-classification of respiratory sounds using optimized s-transform and deep residual networks, IEEE Access 7 ( 2019) 32845–32852. Content-Based Multimedia Indexing (CBMI) (IEEE, 2018), pp. Desainte-Catherine, Automatic detection of patient with respiratory diseases using lung sound analysis, 2018 Int. ![]() Toward a Caring and Humane Technology, Vol. Pelletier, New parameters for respiratory sound classification, CCECE 2003-Canadian Conf. Saryal, Classification of lung sounds using convolutional neural networks, EURASIP J. Hentzler, Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0, J. Allahverdi, Deep learning on computerized analysis of chronic obstructive pulmonary disease, IEEE J. Basu, Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans. On a publicly available dataset (ICBHI) of size 6898 cycles spanning over 5 h, our results can be compared with the state-of-the-art results, in distinguishing two different types of adventitious sounds: crackles and wheezes. Specifically, we propose a respiratory sounds representation technique capable of modeling the dominant frequency range present in such sounds. The proposed approach attempts to distinguish the adventitious sounds (crackles and wheezes) by modeling the human auditory perception of these sounds. Moreover, different sounds are characterized by different frequency ranges that are dominant. The adventitious sounds, crackles and wheezes appear distinct to the human ear. In this paper, we propose an approach that employs filter bank energy-based features and Random Forests to classify lung problem types from respiratory sounds. Research has shown that it is possible to automatically monitor lung health abnormalities through respiratory sounds. Early recognition of lung health abnormalities can facilitate early intervention, and decrease the mortality rate of the infected population. ![]() Lately, a major chunk of the World population has been infected with serious respiratory diseases such as COVID-19. Audio-based healthcare technologies are among the most significant applications of pattern recognition and Artificial Intelligence. ![]()
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