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Extracting Individual Respiratory Signatures from Combined Multi-Subject Mixtures with Varied Breathing Pattern Using Independent Component Analysis with the JADE Algorithm

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posted on 03.11.2020, 21:16 by Shekh Md Mahmudul Islam
Concurrent respiration monitoring of multiple subjects remains a challenge in microwave Doppler radar-based
non-contact physiological sensing technology. Prior research
using Independent component analysis with the JADE
algorithm has been limited to the separation of respiratory
signatures for normal breathing patterns. This paper
investigates the feasibility of using the ICA-JADE algorithm
with a 24-GHz phase comparison monopulse radar transceiver for separating respiratory signatures from combined mixtures of varied breathing patterns. Normal, fast, and slow breathing pattern variations likely to occur due to physiological activity, and emotional stress were used as a basis for assessing separation robustness. Experimental results showed efficacy for recognition of three different breathing patterns, and isolation of respiratory signatures with an accuracy of 100% for normal breathing, 92% for slow breathing, and 83.78% for fast breathing using ICA-JADE. Breathing pattern variations were observed to affect the signal-to-noise ratio through multiple mechanisms, decreasing with an increase in the number of
breathing cycles and associated motion artifacts. Additionally, for removing motion artifacts of fast breathing pattern empirical mode decomposition (EMD) is employed, and for slow breathing pattern, increasing the breathing cycles helps to achieve an accuracy of 89.2% and 94.5% respectively.


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Submitting Author's Institution

University of Hawaii at Manoa

Submitting Author's Country

United States of America