Compressive sensing in electrical impedance tomography for breathing monitoring

Shiraz, Arsam, Khodadad, Davood, Nordebo, Sven, Yerworth, Rebecca, Frerichs, Inez, van Kaam, Anton, Kallio, Merja, Papadouri, Thalia, Bayford, Richard and Demosthenous, Andreas (2019) Compressive sensing in electrical impedance tomography for breathing monitoring. Physiological measurement . ISSN 1361-6579 (Published online first)

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Abstract

Continuous functional thorax monitoring using EIT has been extensively researched. A limiting factor in high temporal resolution, three dimensional, and fast EIT is the handling of the volume of raw impedance data produced for transmission and storage. Owing to the periodicity of breathing that may be reflected in EIT boundary measurements, data dimensionality may be reduced efficiently at the time of sampling using compressed sensing techniques. Measurements using a 32-electrode 48-frame-per-second EIT system from 30 neonates were post-processed to simulate random demodulation acquisition method on 2000 frames for compression ratios (CRs) ranging from 2-100. Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data was used in the subsequent studies. The signal to noise ratio (SNR) for the entire frequency band (0 Hz - 24 Hz) and three local frequency bands were analysed. A breath detection algorithm was applied to traces and the subsequent error-rates were calculated while considering the outcome of the algorithm applied to a down-sampled and linearly interpolated version of the traces as the baseline. SNR degradation was proportional with CR. The mean degradation for 0 Hz - 8 Hz was below ~15 dB for all CRs. The error-rates in the outcome of the breath detection algorithm in the case of decompressed traces were lower than those of the associated down-sampled traces for CR≥25, corresponding to sub-Nyquist rate for breathing. For instance, the mean error-rate associated with CR = 50 was ~60% lower than that of the corresponding down-sampled traces. To the best of our knowledge, no other study has evaluated compressive sensing on boundary impedance data in EIT. While further research should be directed at optimising the acquisition and decompression techniques for this application, this contribution serves as the baseline for future efforts. [Abstract copyright: Creative Commons Attribution license.]

Item Type: Article
Additional Information: ** From PubMed via Jisc Publications Router
Keywords (uncontrolled): breath detection, compressive sensing, electrical impedance tomography
Research Areas: A. > School of Science and Technology > Natural Sciences > Biophysics and Bioengineering group
Item ID: 26304
Notes on copyright: This is an author-created, un-copyedited version of an article accepted for publication/published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-6579/ab0daa. As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately.
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Date Deposited: 22 Mar 2019 09:11
Last Modified: 04 Apr 2019 18:27
URI: https://eprints.mdx.ac.uk/id/eprint/26304

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