  Diaphragmatic electromyogram (EMGdi) contains crucial information about human
respiration therefore can be used to monitor respiratory condition. Although it
is practical to record EMGdi noninvasively and conveniently by placing surface
electrodes over chest skin, extraction of such weak surface EMGdi (sEMGdi) from
great noisy environment is a challenging task, limiting its clinical use
compared with esophageal EMGdi. In this paper, a novel method is presented for
extracting weak sEMGdi signal from high-noise environment based on fast
independent component analysis (FastICA), constrained FastICA and a peel-off
strategy. It is truly a modified version of of progressive FastICA peel-off
(PFP) framework, where the constrained FastICA helps to extract and refine
respiration-related sEMGdi signals, while the peel-off strategy ensures the
complete extraction of weaker sEMGdi components. The method was validated using
both synthetic and clinical signals. It was demonstrated that our method was
able to extract clean sEMGdi signals efficiently with little distortion. It
outperformed state-of-the-art comparison methods in terms of sufficiently high
SIR and CORR at all noise levels when tested on synthetic data, while also
achieved an accuracy of 95.06% and a F2-score of 96.73% for breath
identification on clinical data. The study presents a valuable solution for
noninvasive extraction of sEMGdi signals, providing a convenient and valuable
way of ventilator synchrony with a significant potential in aiding respiratory
rehabilitation and health.
