Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds
Article
Barua, Prabal Datta, Keles, Tugce, Kuluozturk, Mutlu, Kobat, Mehmet Ali, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendr. 2024. "Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds." Neural Computing and Applications. 36 (27), pp. 16857-16871. https://doi.org/10.1007/s00521-024-09895-5
| Article Title | Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds |
|---|---|
| ERA Journal ID | 18089 |
| Article Category | Article |
| Authors | Barua, Prabal Datta, Keles, Tugce, Kuluozturk, Mutlu, Kobat, Mehmet Ali, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendr |
| Journal Title | Neural Computing and Applications |
| Journal Citation | 36 (27), pp. 16857-16871 |
| Number of Pages | 15 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | United Kingdom |
| ISSN | 0941-0643 |
| 1433-3058 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-024-09895-5 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s00521-024-09895-5 |
| Abstract | Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening. |
| Keywords | Asthma disease detection |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420311. Health systems |
| Byline Affiliations | School of Business |
| Firat University, Turkey | |
| Erzurum Technical University, Turkey | |
| National Heart Centre, Singapore | |
| School of Mathematics, Physics and Computing | |
| Centre for Health Research |
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https://research.usq.edu.au/item/z8467/automated-asthma-detection-in-a-1326-subject-cohort-using-a-one-dimensional-attractive-and-repulsive-center-symmetric-local-binary-pattern-technique-with-cough-sounds
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