A machine hearing system for robust cough detection based on a high-level representation of band-specific audio features

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Abstract

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 in two steps. First, a short-term spectral feature set is separately computed in five pre-defined frequency bands: [0,0.5), [0.5,1), [1,1.5), [1.5,2), [2,5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Secondly, 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-life scenarios in terms of noise content.

Results: the system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Characteristic (ROC) curve (AUC), outperforming state-of-the art methods, 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 disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns) and national health systems (by reducing hospitalisations).
Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
Publication statusAccepted/In press - 15 Dec 2018

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Audition
Monitoring
Respiratory system
Patient monitoring
Conveying
Frequency bands
Support vector machines
Feature extraction
Health

Keywords

  • Cough detection
  • machine hearing
  • respiratory care
  • patient monitoring
  • spectral features

Cite this

@article{2196248b05d146ed895c773c08667475,
title = "A machine hearing system for robust cough detection based on a high-level representation of band-specific audio features",
abstract = "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 thepotential 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 in two steps. First, a short-term spectral feature set is separately computed in five pre-defined frequency bands: [0,0.5), [0.5,1), [1,1.5), [1.5,2), [2,5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Secondly, high level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 mslong-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-life scenarios in terms of noise content. Results: the system achieves 92.71{\%} sensitivity, 88.58{\%} specificity, and 90.69{\%} Area Under Receiver Operating Characteristic (ROC) curve (AUC), outperforming state-of-the art methods, 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 disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns) and national health systems (by reducing hospitalisations).",
keywords = "Cough detection, machine hearing, respiratory care, patient monitoring, spectral features",
author = "Jes{\'u}s Monge-{\'A}lvarez and {Hoyos Barcel{\'o}}, Carlos and San-Jos{\'e}-Revuelta, {Luis M.} and Casaseca, {Juan Pablo}",
year = "2018",
month = "12",
day = "15",
language = "English",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE",

}

TY - JOUR

T1 - A machine hearing system for robust cough detection based on a high-level representation of band-specific audio features

AU - Monge-Álvarez, Jesús

AU - Hoyos Barceló, Carlos

AU - San-José-Revuelta, Luis M.

AU - Casaseca, Juan Pablo

PY - 2018/12/15

Y1 - 2018/12/15

N2 - 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 thepotential 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 in two steps. First, a short-term spectral feature set is separately computed in five pre-defined frequency bands: [0,0.5), [0.5,1), [1,1.5), [1.5,2), [2,5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Secondly, high level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 mslong-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-life scenarios in terms of noise content. Results: the system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Characteristic (ROC) curve (AUC), outperforming state-of-the art methods, 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 disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns) and national health systems (by reducing hospitalisations).

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 thepotential 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 in two steps. First, a short-term spectral feature set is separately computed in five pre-defined frequency bands: [0,0.5), [0.5,1), [1,1.5), [1.5,2), [2,5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Secondly, high level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 mslong-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-life scenarios in terms of noise content. Results: the system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Characteristic (ROC) curve (AUC), outperforming state-of-the art methods, 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 disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns) and national health systems (by reducing hospitalisations).

KW - Cough detection

KW - machine hearing

KW - respiratory care

KW - patient monitoring

KW - spectral features

M3 - Article

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

ER -