Evaluation of respiratory sounds using image-based approaches for health measurement applications

Par Conseil national de recherches du Canada

Téléchargement	Voir la version finale : Evaluation of respiratory sounds using image-based approaches for health measurement applications (PDF, 2.3 Mio)
DOI	Trouver le DOI : https://doi.org/10.1109/OJEMB.2022.3202435
Auteur	Rechercher : Cohen-McFarlane, Madison Identifiant ORCID : https://orcid.org/0000-0001-8587-613X; Rechercher : Xi, Pengcheng¹Identifiant ORCID : https://orcid.org/0000-0003-3236-5234; Rechercher : Wallace, Bruce Identifiant ORCID : https://orcid.org/0000-0003-4379-2717; Rechercher : Habashy, Karim; Rechercher : Huq, Saif; Rechercher : Goubran, Rafik; Rechercher : Knoefel, Frank
Affiliation	Conseil national de recherches Canada. Technologies numériques
Format	Texte, Article
Description physique	8 p.
Sujet	acoustic signal processing; audio visualization; convolutional neural network; cough classification; respiratory classification
Résumé	Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.
Date de publication	2022-09-13
Maison d’édition	Institute of Electrical and Electronics Engineers (IEEE)
Licence	Creative Commons, Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.fr
Dans	IEEE Open Journal of Engineering in Medicine and Biology.
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	a5744274-3fe9-4323-9b1f-9f844664c9cc
Enregistrement créé	2022-09-26
Enregistrement modifié	2022-09-26

Date de modification :: 2026-01-13