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Title: Distance-based detection of cough, wheeze, and breath sounds on wearable devices
Authors: Xue, Bing
Shi, Wen
Chotirmall, Sanjay Haresh
Koh, Vivian Ci Ai
Ang, Yi Yang
Tan, Rex Xiao
Ser, Wee
Keywords: Science::Medicine
Issue Date: 2022
Source: 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-.
Journal: Sensors 
Abstract: 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.
ISSN: 1424-8220
DOI: 10.3390/s22062167
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
Rights: © 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 (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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