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

Jesús Monge-Álvarez, Carlos Hoyos Barceló, Luis M. San-José-Revuelta, Pablo Casaseca-de-la-Higuera

Research output: Contribution to journalArticle

1 Citation (Scopus)
193 Downloads (Pure)

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
Pages (from-to)2319-2330
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume66
Issue number8
Early online date20 Dec 2018
DOIs
Publication statusPublished - 31 Aug 2019

Keywords

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

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