Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements

McDermott, Barry; Dunne, Eoghan; O’Halloran, Martin; Porter, Emily; Santorelli, Adam

doi:10.1007/978-3-030-21293-3_12

Cited by 5 publications

(14 citation statements)

References 43 publications

(54 reference statements)

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“…However, the results are still strong for normal versus bleed with mean accuracy of 87% for both the human and the simulated data sets. These results imply the MFSD-EIT with classification approach may indeed have application in areas such as TBI and build upon the work of [13], [14] where ML was applied to EIT measurement frames in detecting intracranial haemorrhage.…”

Section: Normal Versus Bleedmentioning

confidence: 69%

“…SVMs are a group of ML algorithms often used for binary classification but can be adapted for use in multiclass classification [30]. SVM classification has been successfully demonstrated in previous biomedical applications including the use of microwaves for detection of breast cancer [35]- [37], impedance spectroscopy for the detection of prostate cancer [38], and work by our group into EIT measurement frames for the detection of brain haemorrhage [13], [14]. The SVM is trained with features from labelled observations (supervised learning) generating a trained model.…”

Section: A Svm Classifiersmentioning

confidence: 99%

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Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

et al. 2020

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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 fourlayer 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. 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.

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Section: Normal Versus Bleedmentioning

confidence: 69%

Section: A Svm Classifiersmentioning

confidence: 99%

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

et al. 2020

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“…Furthermore, a preliminary analysis that compared simulated voltages generated from models utilizing an aggregate layer with models using discrete layers for scalp and skull demonstrated that the primary impact is an attenuation factor. This simplification has also been applied in other studies [27,28,38]. As these layers are close to the measuring electrodes, employing designated layers for scalp and skull should be considered in future studies to further increase the approximation to reality.…”

Section: Scalp and Skull Aggregatementioning

confidence: 99%

“…We identified 22 studies related to EIT-based stroke detection ; 8 of which investigated some form of stroke differentiation using an ML classifier [21][22][23][24][25][26][27][28]. Out of these works, none attempted classification using models with more than 4 layers and more than 4 lesion locations or studied the impact of including ventricles in the model.…”

Section: Introductionmentioning

confidence: 99%

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Culpepper,

Lee,

Santorelli

et al. 2023

Biomed. Phys. Eng. Express

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Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

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“…An 80 dB SNR was chosen for the simulations because previous reports have said that this effective SNR may be required for imaging scenarios of the same challenge level as functional EIT-BI, though may be successful with systems having 30-40 dB SNR [70]. The averaging of 10 80 dB SNR measurements in the simulations was selected to reduce the computation time for simulating 100-1,000 averaged measurements for a 60 dB and 40 dB system, respectively.…”

Section: Simulationsmentioning

confidence: 99%

Improving Electrical Impedance Tomography Imaging of the Brain

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Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements

Cited by 5 publications

References 43 publications

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Improving Electrical Impedance Tomography Imaging of the Brain

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