Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography
Electrical impedance tomography is an emerging technique for brain disease detection. Generally, it requires that electrodes should be equidistantly placed around the detected region. However, this may be not possible for some patients who are undergoing post-surgical monitoring. Aiming at this prob...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10083110/ |
| _version_ | 1797852985831718912 |
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| author | Zhiwei Tian Yanyan Shi Can Wang Meng Wang Ke Shen |
| author_facet | Zhiwei Tian Yanyan Shi Can Wang Meng Wang Ke Shen |
| author_sort | Zhiwei Tian |
| collection | DOAJ |
| description | Electrical impedance tomography is an emerging technique for brain disease detection. Generally, it requires that electrodes should be equidistantly placed around the detected region. However, this may be not possible for some patients who are undergoing post-surgical monitoring. Aiming at this problem, four kinds of non-uniform electrode arrangements are developed. To accurately detect the location of intracranial hemorrhage, a novel classification method based on a priori information of electrode arrangement is also proposed in this paper. According to the electrode arrangement information, the weight which corresponds to different kinds of electrode arrangement is separately determined during the training process. The proposed method is quantitatively evaluated with basic test dataset, test datasets under noise interruption, test datasets in the case of large contact impedance, test datasets with conductivity variation in different layers, and test datasets when considering modeling error and double inclusions. Comparisons with general classification methods are also conducted. The results show that the proposed method with residual network incorporated outperforms the classification methods of fully connected neural network and residual network. For all the test datasets, the results show that the accuracy is higher than 0.9 and the specificity reaches as high as 1 when the proposed method incorporating residual network is used. |
| first_indexed | 2024-04-09T19:43:16Z |
| format | Article |
| id | doaj.art-72b81f55ea4e473f8d94afca508fe046 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T19:43:16Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-72b81f55ea4e473f8d94afca508fe0462023-04-03T23:00:36ZengIEEEIEEE Access2169-35362023-01-0111313553136410.1109/ACCESS.2023.326257510083110Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance TomographyZhiwei Tian0https://orcid.org/0000-0002-7966-3709Yanyan Shi1https://orcid.org/0000-0003-2759-0015Can Wang2Meng Wang3https://orcid.org/0000-0002-2470-792XKe Shen4College of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, ChinaCollege of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, ChinaCollege of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, ChinaCollege of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, ChinaCollege of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, ChinaElectrical impedance tomography is an emerging technique for brain disease detection. Generally, it requires that electrodes should be equidistantly placed around the detected region. However, this may be not possible for some patients who are undergoing post-surgical monitoring. Aiming at this problem, four kinds of non-uniform electrode arrangements are developed. To accurately detect the location of intracranial hemorrhage, a novel classification method based on a priori information of electrode arrangement is also proposed in this paper. According to the electrode arrangement information, the weight which corresponds to different kinds of electrode arrangement is separately determined during the training process. The proposed method is quantitatively evaluated with basic test dataset, test datasets under noise interruption, test datasets in the case of large contact impedance, test datasets with conductivity variation in different layers, and test datasets when considering modeling error and double inclusions. Comparisons with general classification methods are also conducted. The results show that the proposed method with residual network incorporated outperforms the classification methods of fully connected neural network and residual network. For all the test datasets, the results show that the accuracy is higher than 0.9 and the specificity reaches as high as 1 when the proposed method incorporating residual network is used.https://ieeexplore.ieee.org/document/10083110/Electrical impedance tomographyelectrode arrangementclassificationneural network |
| spellingShingle | Zhiwei Tian Yanyan Shi Can Wang Meng Wang Ke Shen Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography IEEE Access Electrical impedance tomography electrode arrangement classification neural network |
| title | Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography |
| title_full | Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography |
| title_fullStr | Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography |
| title_full_unstemmed | Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography |
| title_short | Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography |
| title_sort | classification of hemorrhage using priori information of electrode arrangement with electrical impedance tomography |
| topic | Electrical impedance tomography electrode arrangement classification neural network |
| url | https://ieeexplore.ieee.org/document/10083110/ |
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