RT info:eu-repo/semantics/conferenceObject
T1 Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks
A1 Amado Caballero, Patricia
A1 Garmendia-Leiza, J.R.
A1 Aguilar-García, M.D.
A1 Martínez-Fernández-de-Septiem, C.
A1 San José Revuelta, Luis Miguel
A1 García-Ruano, A.
A1 Alberola López, Carlos
A1 Casaseca de la Higuera, Juan Pablo
K1 Detección
K1 CAD
K1 Procesado de señal
K1 Machine learning
K1 Deep learning
K1 Respiratory diseases
K1 cough
K1 audio analysis
K1 CNN
K1 XAI
K1 occlusion maps
K1 3306 Ingeniería y Tecnología Eléctricas
AB This study explores the feasibility of employing eXplainable Artificial Intelligence XAI methodologies for  the analysis of cough patterns in respiratory diseases. A cohort of 20 adult patients, all presenting persistent cough as a symptom of respiratory disease, was monitored for 24 hours using a smartphone. The audio signals underwent frequency domain transformation to yield 1-second spectrograms, subsequently processed by a CNN to detect cough events. Quantitative analysis of spectrogram regions relevant for cough detection highlighted by occlusion maps, revealed significant differences between patient groups. Notably, distinctions were observed between the Chronic Obstructive Pulmonary Disease (COPD) patient group and groups with other respiratory pathologies, both chronic and non-chronic. In conclusion, interpretability analysis methods applied to neural networks offer insights into cough-related distinctions among patients with varying respiratory conditions.
PB IEEE, Institute of Electrical and Electronics Engineers
SN 979-8-3503-7149-9
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/72906
UL https://uvadoc.uva.es/handle/10324/72906
LA eng
NO 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, Florida, USA, July 15-19, 2024.
NO Producción Científica
DS UVaDOC
RD 22-dic-2024