RT journal article
T1 Cough Detection Using Acceleration Signals and Deep Learning Techniques
A1 Sánchez Morillo, Daniel
A1 Sales Lérida, Diego
A1 Priego Torres, Blanca María
A1 León Jiménez, Antonio
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 cough
K1 deep learning
K1 accelerometer
K1 mechano-acoustic signals
K1 respiratory sounds
AB Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions.
PB MDPI
SN 2079-9292
YR 2024
FD 2024-06-20
LK http://hdl.handle.net/10498/32832
UL http://hdl.handle.net/10498/32832
LA eng
NO Grant PID2021-126810OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026