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).
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 language | English |
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Pages (from-to) | 2319-2330 |
Number of pages | 12 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 66 |
Issue number | 8 |
Early online date | 20 Dec 2018 |
DOIs | |
Publication status | Published - 31 Aug 2019 |
Keywords
- Cough detection
- machine hearing
- respiratory care
- patient monitoring
- spectral features