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