RT info:eu-repo/semantics/article
T1 A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features
A1 Monge Álvarez, Jesús
A1 Hoyos-Barceló, Carlos
A1 San José Revuelta, Luis Miguel
A1 Casaseca de la Higuera, Juan Pablo
K1 Ingeniería Biomédica
K1 Procesado de señal
K1 Bioingeniería
K1 Computación
K1 Cough detection, machine hearing, respiratory care, patient monitoring, spectral features
K1 1203.25 Diseño de Sistemas Sensores
K1 3306 Ingeniería y Tecnología Eléctricas
AB Cough is a protective reflex conveying information on the state of the respiratory system. Cough assessment has been limited so far to subjective measurement tools or uncomfortable (i.e., non-wearable) cough monitors. This limits the potential of real-time cough monitoring to improve respiratory care. Objective: This paper presents a machine hearing system for audio-based robust cough segmentation that can be easily deployed in mobile scenarios. Methods: Cough detection is performed intwo steps. First, a short-term spectral feature set is separately computed in five predefined frequency bands: [0, 0.5), [0.5, 1), [1, 1.5), [1.5, 2), and [2, 5.5125] kHz. Feature selection and combination are then applied to make the short-termfeature set robust enough in different noisy scenarios. Second, high-level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 ms long-term frames. Finally, cough detection is carried out using a support vector machine trained with data from different noisy scenarios. The system is evaluated using a patient signal database which emulates three real-lifescenarios in terms of noise content. Results: The system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Charcteristic (ROC) curve (AUC), outperforming state-of-the-art methods. Conclusion: Our research outcome paves the way to create a device for cough monitoring in real-life situations. Significance: Our proposal is aligned with a more comfortable and less disruptivepatientmonitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical nderstanding of cough patterns), and national health systems (by reducing hospitalizations).
PB IEEE
SN 0018-9294
YR 2019
FD 2019-08
LK https://uvadoc.uva.es/handle/10324/71260
UL https://uvadoc.uva.es/handle/10324/71260
LA eng
NO IEEE Transactions on Biomedical Engineering, August 2019, Vol. 66, Issue 8, pp. 2319-2330.
NO Producción Científica
DS UVaDOC
RD 24-nov-2024