Respiratory activity classification based on ballistocardiogram analysis [r-libre/2850]

Ben Nasr, Mohamed Chiheb; Ben Jebara, Sofia; Otis, Samuel; Abdulrazak, Bessam et Mezghani, Neila (2020). Respiratory activity classification based on ballistocardiogram analysis. Dans The Impact of Digital Technologies on Public Health in Developed and Developing Countries, vol. 12157 (p. 79-88). Springer. ISBN 978-3-030-51517-1 https://doi.org/10.1007/978-3-030-51517-1_7

Fichier(s) associé(s) à ce document :

PDF - ChihebBenNasr2020.pdf
Contenu du fichier : Version de l'éditeur
Licence : Creative Commons CC BY.

Télécharger

Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Ballistocardiogram signals describe the mechanical activity of the heart. It can be measured by an intelligent mattress in a totally unobtrusive way during periods of rest in bed or sitting on a chair. The BCG signals are highly vulnerable to artefacts such as noise and movement making useful information like respiratory activities difficult to extract. The purpose of this study is to investigate a classification method to distinguish between seven types of respiratory activities such as normal breathing, cough and hold breath. We propose a feature selection method based on a spectral analysis namely spectral flatness measure (SFM) and spectral centroid (SC). The classification is carried out using the nearest neighbor classifier. The proposed method is able to discriminate between the seven classes with the accuracy of 94% which shows its usefulness in context of Telemedicine.
Adresse de la version officielle :	https://link.springer.com/chapter/10.1007/978-3-03...
Déposant:	Ayena, Johannes
Responsable :	Johannes Ayena
Dépôt :	12 janv. 2023 21:24
Dernière modification :	12 janv. 2023 21:24

Actions (connexion requise)

RÉVISER