Neural networks for classification of strokes in electrical impedance tomography on a 3D head model

Valentina Candiani, Matteo Santacesaria

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
20 Downloads (Pure)


We consider the problem of the detection of brain hemorrhages from three-dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent treatment for which EIT might provide a portable and quick diagnosis. We employ two neural network architectures - a fully connected and a convolutional one - for the classification of hemorrhagic and ischemic strokes. The networks are trained on a dataset with 40000 samples of synthetic electrode measurements generated with the complete electrode model on realistic heads with a 3-layer structure. We consider changes in head anatomy and layers, electrode position, measurement noise and conductivity values. We then test the networks on several datasets of unseen EIT data, with more complex stroke modeling (different shapes and volumes), higher levels of noise and different amounts of electrode misplacement. On most test datasets we achieve ≥90% average accuracy with fully connected neural networks, while the convolutional ones display an average accuracy ≥80%. Despite the use of simple neural network architectures, the results obtained are very promising and motivate the applications of EIT-based classification methods on real phantoms and ultimately on human patients.
Original languageEnglish
Article number10.3934/mine.2022029
Pages (from-to)1-22
Number of pages22
JournalMathematics in Engineering
Issue number4
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


  • electrical impedance tomography
  • classification of brain strokes
  • fully connected neural networks
  • convolutional neural networks
  • computational head model


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