Abstract
In this work, we investigate the possibility of employing sparse reconstruction framework for the separation of cardiac and respiratory signal components from the bioimpedance measurements. The signal decomposition is complicated by the nonstationarity of the signal and overlapping of their spectra. The signal has a harmonic structure, which is sparse in the spectral domain. We approach the problem by considering a dictionary with integrated wideband elements describing spectral components of the considered signal. The parameter estimation task is solved by the means of sparse reconstruction where solving the optimization problem returns a sparse vector of relevant dictionary atoms.DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21844
Highlights
Electrical bioimpedance (EBI) measurements based applications for medical signal monitoring can provide interesting alternative to the conventional approaches due to noninvasiveness and cost-effectiveness
We propose to reconstruct the relevant components of the EBI signal by considering wideband dictionary elements introduced in [15], [16]
As we can see from the figure, the resulting estimate follows the actual frequency of the cardiac component closely and mean-square error (MSE) as compared to ECG measurements is 0.0007
Summary
Electrical bioimpedance (EBI) measurements based applications for medical signal monitoring can provide interesting alternative to the conventional approaches due to noninvasiveness and cost-effectiveness. It is more interesting to try to extract both the cardiac and respiratory signals Different approaches to this problem have been proposed in the recent literature including adaptive filtering [6], adaptive phase-locked loop [7], method based on the signal shape-locked-loop decomposer solution [8], principal and independent component analysis [9] and artificial neural networks [10]. Considering the variety of the proposed solutions, it is interesting to note that there is still no clearly established method available for extraction and separation of cardiac and respiration signal components, partly due to the problems mentioned above, and partly due to the variations in the anatomy and physiology of human beings and their behaviour at various times and in physical and mental situations. By refining the estimate through iteratively zooming into active parts of the spectra, the proposed algorithm is able to estimate signal parameters and reconstruct the signal without any prior knowledge
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