ArticleBiomedical engineering online2017
Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography.
Article in Biomedical engineering online, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dynamic Cerebral Perfusion Electrical Impedance Tomography: A Neuroimaging Technique for Bedside Cerebral Perfusion Monitoring During Mannitol Dehydration.Bioengineering (Basel, Switzerland) · 2025Article
- Reduction of Spike-like Noise in Clinical Practice for Thoracic Electrical Impedance Tomography Using Robust Principal Component Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Advances in electrical impedance tomography-based brain imaging.Military Medical Research · 2022Review
- Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data.Frontiers in medicine · 2022Article
- Real-Time Detection of Hemothorax and Monitoring its Progression in a Piglet Model by Electrical Impedance Tomography: A Feasibility Study.BioMed research international · 2020Article
- An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition.Biomedical engineering online · 2019Article
- Comparison of electrical impedance tomography and intracranial pressure during dehydration treatment of cerebral edema.NeuroImage. Clinical · 2019Article
- Combing signal processing methods with algorithm priori information to produce synergetic improvements on continuous imaging of brain electrical impedance tomography.Scientific reports · 2018Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundElectrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data.
methodsWeighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings.
resultsThe proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%.
conclusionsIn this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion.
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