McDermott, B., Elahi, A., Santorelli, A. et al. (3 more authors) (2020) Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis. Physiological Measurement, 41 (7). ARTN 075010. ISSN 0967-3334
Abstract
Objective: Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning.
Methods: Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz–100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values.
Main results: An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data.
Conclusion: Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes.
Significance: The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of an article published in Physiological Measurement. Uploaded in accordance with the publisher's self-archiving policy. This Accepted Manuscript is available for reuse under a CC BY-NC-ND licence. |
Keywords: | electrical impedance tomography, reconstruction algorithm, stroke imaging |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Mar 2024 14:02 |
Last Modified: | 15 Mar 2024 14:02 |
Published Version: | https://iopscience.iop.org/article/10.1088/1361-65... |
Status: | Published |
Publisher: | IOP Publishing |
Identification Number: | 10.1088/1361-6579/ab9e54 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210311 |
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