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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (6), p.2167
Main Authors: Xue, Bing, Shi, Wen, Chotirmall, Sanjay H, Koh, Vivian Ci Ai, Ang, Yi Yang, Tan, Rex Xiao, Ser, Wee
Format: Article
Language:English
Subjects:
Accuracy
acoustic signal processing
Acoustics
Algorithms
Chronic obstructive pulmonary disease
Classification
Cough - diagnosis
Data acquisition
Data processing
distance classification
Embedded systems
feature selection algorithm
health monitoring
Humans
Methods
Neural networks
Respiration
Respiratory Sounds - diagnosis
Signal processing
Signal Processing, Computer-Assisted
Smart sensors
Sound
Time series
Wavelet transforms
Wearable computers
wearable devices
Wearable Electronic Devices
Wearable technology
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Summary: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
1424-8220
DOI:10.3390/s22062167