Shiraz, A., Khodadad, D., Nordebo, S., Yerworth, R., Frerichs, I., van Kaam, A., … Demosthenous, A. (2019). Compressive sensing in electrical impedance tomography for breathing monitoring. Physiological Measurement, 40(3), 34010. https://doi.org/10.1088/1361-6579/ab0daa
Compressive sensing in electrical impedance tomography for breathing monitoring
|Author:||Shiraz, A.1; Khodadad, D.2; Nordebo, S.3;|
1Department of Electronic and Electrical Engineering, University College London, London, United Kingdom
2Department of Mechanical Engineering, Örebro University, SE-701 82 Örebro, Sweden
3Department of Physics and Electrical Engineering, Linnaeus University, Växjö, Sweden
4Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
5Department of Anaesthesiology and Intensive Care Medicine, University Medical Centre Schleswig-Holstein, Kiel, Germany
6Department of Neonatology, Emma Children’s Hospital, Academic Medical Center, Amsterdam, The Netherlands
7PEDEGO Research Unit, Medical Research Center Oulu, University of Oulu, Oulu, Finland
8Department of Children and Adolescents, Oulu University Hospital, Finland
9Neonatal Intensive Care Unit, ‘Archbishop Makarios III’ Hospital, Ministry of Health, Nicosia, Cyprus
10Department of Natural Sciences, Middlesex University, London, United Kingdom
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019093030432
|Publish Date:|| 2019-09-30
Objective: Electrical impedance tomography (EIT) is a functional imaging technique in which cross-sectional images of structures are reconstructed based on boundary trans-impedance measurements. Continuous functional thorax monitoring using EIT has been extensively researched. Increasing the number of electrodes, number of planes and frame rate may improve clinical decision making. Thus, a limiting factor in high temporal resolution, 3D and fast EIT is the handling of the volume of raw impedance data produced for transmission and its subsequent storage. Owing to the periodicity (i.e. sparsity in frequency domain) of breathing and other physiological variations that may be reflected in EIT boundary measurements, data dimensionality may be reduced efficiently at the time of sampling using compressed sensing techniques. This way, a fewer number of samples may be taken.
Approach: Measurements using a 32-electrode, 48-frames-per-second EIT system from 30 neonates were post-processed to simulate random demodulation acquisition method on 2000 frames (each consisting of 544 measurements) for compression ratios (CRs) ranging from 2 to 100. Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data (i.e. sum of all 544 measurements in each frame) 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.
Main results: SNR degradation was generally proportional with CR. The mean degradation for 0 Hz–8 Hz (of interest for the target physiological variations) 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 associated with the corresponding down-sampled traces for CR ≥ 25, corresponding to sub-Nyquist rate for breathing frequency. For instance, the mean error-rate associated with CR = 50 was ~60% lower than that of the corresponding down-sampled traces.
Significance: To the best of our knowledge, no other study has evaluated the applicability of compressive sensing techniques on raw 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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
We acknowledge the funding from the European Union’s Framework program for research and innovation Horizon 2020 (CRADL,Grant No.668259). We would like to also thank all our colleagues in the CRADL project.
© 2019 Institute of Physics and Engineering in Medicine. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.