Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typic...

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Autores principales:	Rizwana Zulfiqar, Fiaz Majeed, Rizwana Irfan, Hafiz Tayyab Rauf, Elhadj Benkhelifa, Abdelkader Nasreddine Belkacem
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	respiratory sounds abnormal respiratory sounds continuous adventitious sounds (CAS) discontinuous adventitious sounds (DAS) deep CNN Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/9cb31d04994845418572fbf112315758
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spelling	oai:doaj.org-article:9cb31d04994845418572fbf1123157582021-11-17T05:04:34ZAbnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition2296-858X10.3389/fmed.2021.714811https://doaj.org/article/9cb31d04994845418572fbf1123157582021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.714811/fullhttps://doaj.org/toc/2296-858XRespiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.Rizwana ZulfiqarFiaz MajeedRizwana IrfanHafiz Tayyab RaufElhadj BenkhelifaAbdelkader Nasreddine BelkacemFrontiers Media S.A.articlerespiratory soundsabnormal respiratory soundscontinuous adventitious sounds (CAS)discontinuous adventitious sounds (DAS)deep CNNMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	respiratory sounds abnormal respiratory sounds continuous adventitious sounds (CAS) discontinuous adventitious sounds (DAS) deep CNN Medicine (General) R5-920
spellingShingle	respiratory sounds abnormal respiratory sounds continuous adventitious sounds (CAS) discontinuous adventitious sounds (DAS) deep CNN Medicine (General) R5-920 Rizwana Zulfiqar Fiaz Majeed Rizwana Irfan Hafiz Tayyab Rauf Elhadj Benkhelifa Abdelkader Nasreddine Belkacem Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
description	Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.
format	article
author	Rizwana Zulfiqar Fiaz Majeed Rizwana Irfan Hafiz Tayyab Rauf Elhadj Benkhelifa Abdelkader Nasreddine Belkacem
author_facet	Rizwana Zulfiqar Fiaz Majeed Rizwana Irfan Hafiz Tayyab Rauf Elhadj Benkhelifa Abdelkader Nasreddine Belkacem
author_sort	Rizwana Zulfiqar
title	Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
title_short	Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
title_full	Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
title_fullStr	Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
title_full_unstemmed	Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition
title_sort	abnormal respiratory sounds classification using deep cnn through artificial noise addition
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/9cb31d04994845418572fbf112315758
work_keys_str_mv	AT rizwanazulfiqar abnormalrespiratorysoundsclassificationusingdeepcnnthroughartificialnoiseaddition AT fiazmajeed abnormalrespiratorysoundsclassificationusingdeepcnnthroughartificialnoiseaddition AT rizwanairfan abnormalrespiratorysoundsclassificationusingdeepcnnthroughartificialnoiseaddition AT hafiztayyabrauf abnormalrespiratorysoundsclassificationusingdeepcnnthroughartificialnoiseaddition AT elhadjbenkhelifa abnormalrespiratorysoundsclassificationusingdeepcnnthroughartificialnoiseaddition AT abdelkadernasreddinebelkacem abnormalrespiratorysoundsclassificationusingdeepcnnthroughartificialnoiseaddition
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Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

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