Distance-based detection of cough, wheeze, and breath sounds on wearable devices
Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are re...
| Glavni autori: | , , , , , , |
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| Daljnji autori: | |
| Format: | Journal Article |
| Jezik: | English |
| Izdano: |
2022
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| Teme: | |
| Online pristup: | https://hdl.handle.net/10356/161310 |
| _version_ | 1826128729340903424 |
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| author | Xue, Bing Shi, Wen Chotirmall, Sanjay Haresh Koh, Vivian Ci Ai Ang, Yi Yang Tan, Rex Xiao Ser, Wee |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Xue, Bing Shi, Wen Chotirmall, Sanjay Haresh Koh, Vivian Ci Ai Ang, Yi Yang Tan, Rex Xiao Ser, Wee |
| author_sort | Xue, Bing |
| collection | NTU |
| description | Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices. |
| first_indexed | 2024-10-01T07:29:23Z |
| format | Journal Article |
| id | ntu-10356/161310 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:29:23Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1613102023-03-05T16:54:51Z Distance-based detection of cough, wheeze, and breath sounds on wearable devices Xue, Bing Shi, Wen Chotirmall, Sanjay Haresh Koh, Vivian Ci Ai Ang, Yi Yang Tan, Rex Xiao Ser, Wee Lee Kong Chian School of Medicine (LKCMedicine) Science::Medicine Acoustic Signal Processing Distance Classification Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices. Published version 2022-08-24T07:00:52Z 2022-08-24T07:00:52Z 2022 Journal Article Xue, B., Shi, W., Chotirmall, S. H., Koh, V. C. A., Ang, Y. Y., Tan, R. X. & Ser, W. (2022). Distance-based detection of cough, wheeze, and breath sounds on wearable devices. Sensors, 22(6), 2167-. https://dx.doi.org/10.3390/s22062167 1424-8220 https://hdl.handle.net/10356/161310 10.3390/s22062167 35336338 2-s2.0-85125901509 6 22 2167 en Sensors © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Science::Medicine Acoustic Signal Processing Distance Classification Xue, Bing Shi, Wen Chotirmall, Sanjay Haresh Koh, Vivian Ci Ai Ang, Yi Yang Tan, Rex Xiao Ser, Wee Distance-based detection of cough, wheeze, and breath sounds on wearable devices |
| title | Distance-based detection of cough, wheeze, and breath sounds on wearable devices |
| title_full | Distance-based detection of cough, wheeze, and breath sounds on wearable devices |
| title_fullStr | Distance-based detection of cough, wheeze, and breath sounds on wearable devices |
| title_full_unstemmed | Distance-based detection of cough, wheeze, and breath sounds on wearable devices |
| title_short | Distance-based detection of cough, wheeze, and breath sounds on wearable devices |
| title_sort | distance based detection of cough wheeze and breath sounds on wearable devices |
| topic | Science::Medicine Acoustic Signal Processing Distance Classification |
| url | https://hdl.handle.net/10356/161310 |
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