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

Research output: Contribution to journalArticle

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).
LanguageEnglish
JournalIEEE Transactions on Biomedical Engineering
StateAccepted/In press - 15 Dec 2018

Fingerprint

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",

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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

T2 - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

ER -