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 ORCID: https://orcid.org/0000-0001-8863-6385 and Demosthenous, Andreas (2019) Compressive sensing in electrical impedance tomography for breathing monitoring. Physiological Measurement, 40 (3) , 034010. ISSN 0967-3334 [Article] (doi:10.1088/1361-6579/ab0daa)

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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.]

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