dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Jiménez Fernández, Ángel Francisco | es |
dc.creator | Domínguez Morales, Manuel Jesús | es |
dc.creator | Jiménez Moreno, Gabriel | es |
dc.date.accessioned | 2019-12-26T11:35:48Z | |
dc.date.available | 2019-12-26T11:35:48Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Domínguez Morales, J.P., Jiménez Fernández, Á.F., Domínguez Morales, M.J. y Jiménez Moreno, G. (2017). Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors. IEEE Transactions on Biomedical Circuits and Systems, 12 (1), 24-34. | |
dc.identifier.issn | 1932-4545 | es |
dc.identifier.uri | https://hdl.handle.net/11441/91257 | |
dc.description.abstract | Auscultation is one of the most used techniques for
detecting cardiovascular diseases, which is one of the main causes
of death in the world. Heart murmurs are the most common abnormal
finding when a patient visits the physician for auscultation.
These heart sounds can either be innocent, which are harmless, or
abnormal, which may be a sign of a more serious heart condition.
However, the accuracy rate of primary care physicians and expert
cardiologists when auscultating is not good enough to avoid most
of both type-I (healthy patients are sent for echocardiogram) and
type-II (pathological patients are sent home without medication or
treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between
healthy people and pathological patients using a neuromorphic
auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks
have been trained with the heart murmur information contained in
heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples
are segmented and preprocessed using the neuromorphic auditory
sensor to decompose their audio information into frequency
bands and, after that, sonogram images with the same size are
generated. These images have been used to train and test different
convolutional neural network architectures. The best results
have been obtained with a modified version of the AlexNet model,
achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%,
PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid
cardiologists and primary care physicians in the auscultation process,
improving the decision making task and reducing type-I and
type-II errors. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2016-77785-P | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Transactions on Biomedical Circuits and Systems, 12 (1), 24-34. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Audio processing | es |
dc.subject | Caffe | es |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | Deep learning | es |
dc.subject | Heart murmur | es |
dc.subject | Neuromorphic sensor | es |
dc.subject | Pattern recognition | es |
dc.title | Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | TEC2016-77785-P | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8048493 | es |
dc.identifier.doi | 10.1109/TBCAS.2017.2751545 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
idus.format.extent | 11 | es |
dc.journaltitle | IEEE Transactions on Biomedical Circuits and Systems | es |
dc.publication.volumen | 12 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 24 | es |
dc.publication.endPage | 34 | es |