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Pubmed for Handhelds

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  • Title: Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices.
    Author: Xue B, Shi W, Chotirmall SH, Koh VCA, Ang YY, Tan RX, Ser W.
    Journal: Sensors (Basel); 2022 Mar 10; 22(6):. PubMed ID: 35336338.
    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.
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