Crackle and wheeze detection in lung sound signals using convolutional neural networks

dc.contributor.authorFaustino, Pedro
dc.contributor.authorOliveira, Jorge
dc.contributor.authorCoimbra, Miguel
dc.date.accessioned2022-05-13T08:58:14Z
dc.date.available2022-05-13T08:58:14Z
dc.date.issued2021-11-01
dc.description.abstractRespiratory diseases are among the leading causes of death worldwide. Preventive measures are essential to avoid and increase the odds of a successful recovery. An important screening tool is pulmonary auscultation, an inexpensive, noninvasive and safe method to assess the mechanics and dynamics of the lungs. On the other hand, it is a difficult task for a human listener since some lung sound events have a spectrum of frequencies outside of the human hearing ability. Thus, computer assisted decision systems might play an important role in the detection of abnormal sounds, such as crackle or wheeze sounds. In this paper, we propose a novel system, which is not only able to detect abnormal lung sound events, but it is also able to classify them. Furthermore, our system was trained and tested using the publicly available ICBHI 2017 challenge dataset, and using the metrics proposed by the challenge, thus making our framework and results easily comparable. Using a Mel Spectrogram as an input feature for our convolutional neural network, our system achieved results in line with the current state of the art, an accuracy of 43%, and a sensitivity of 51%.pt_PT
dc.identifier.citationFaustino, P., Oliveira, J., & Coimbra, M. (2021). Crackle and wheeze detection in lung sound signals using convolutional neural networks. In 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 31th October-4th November 2021 (pp. 345-348). https//doi.org/10.1109/EMBC46164.2021.9630391. Repositório Institucional UPT. http://hdl.handle.net/11328/4088pt_PT
dc.identifier.doi10.1109/EMBC46164.2021.9630391pt_PT
dc.identifier.issn978-1-7281-1179-7/21/$31.00
dc.identifier.urihttp://hdl.handle.net/11328/4088
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.rightsrestricted accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRespiratory diseasespt_PT
dc.subjectPulmonary auscultationpt_PT
dc.titleCrackle and wheeze detection in lung sound signals using convolutional neural networkspt_PT
dc.typeconferenceObjectpt_PT
degois.publication.firstPage345pt_PT
degois.publication.lastPage348pt_PT
degois.publication.locationVirtual Conferencept_PT
degois.publication.title43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)pt_PT
dspace.entity.typePublicationen
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