Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices.

Bing Xue,Wen Shi,Rex Xiao Tan,Yi Yang Ang,Vivian Ci Ai Koh,Wee Ser,Sanjay H Chotirmall

doi:10.3390/s22062167

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.

Highlights

Acoustic signal processing is a growing area of focus in healthcare and biomedicine.The characteristics of acoustic signals, especially that of abnormal breath sounds, provide clinicians with valuable information on respiratory diseases [1]
This study addresses the challenges of existing cough or wheeze detection studies, attempting to find an optimal acoustic signal processing method for implementation in a wearable device
This study proposed a kernel-like detection algorithm for classifying acoustic time

Summary

Introduction

Acoustic signal processing is a growing area of focus in healthcare and biomedicine. The characteristics of acoustic signals, especially that of abnormal breath sounds, provide clinicians with valuable information on respiratory diseases [1]. Clinicians use stethoscopes as an indispensable tool for healthcare delivery for their reliability and efficiency in aiding the investigation of bodily sounds. The interpretation of breath sounds heard through a stethoscope is user dependent, and its single examination mode of measurement disallows continuous monitoring of daily symptoms and fluctuations [2]. With the progression of sensing technology development, wearable devices are available as promising solutions. They facilitate automatic and continuous acoustic analysis and assist clinicians in evaluating the effectiveness of a prescribed intervention [3]

Methods

Discussion

Conclusion