ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation
Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based predict...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10312752/ |
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| author | Barna Ghosh Khaleda Akhter Sathi Md. Kamal Hosain Md. Azad Hossain M. Ali Akber Dewan Abbas Z. Kouzani |
| author_facet | Barna Ghosh Khaleda Akhter Sathi Md. Kamal Hosain Md. Azad Hossain M. Ali Akber Dewan Abbas Z. Kouzani |
| author_sort | Barna Ghosh |
| collection | DOAJ |
| description | Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future. |
| first_indexed | 2024-03-09T02:04:06Z |
| format | Article |
| id | doaj.art-b8e5b253302043e9b3aab71097cf2ed8 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-09T02:04:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-b8e5b253302043e9b3aab71097cf2ed82023-12-08T00:00:25ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01314713472410.1109/TNSRE.2023.333125810312752ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic StimulationBarna Ghosh0https://orcid.org/0009-0006-4859-9915Khaleda Akhter Sathi1https://orcid.org/0000-0003-0031-9284Md. Kamal Hosain2https://orcid.org/0000-0001-7495-3937Md. Azad Hossain3https://orcid.org/0000-0002-8251-5168M. Ali Akber Dewan4https://orcid.org/0000-0001-6347-7509Abbas Z. Kouzani5https://orcid.org/0000-0002-6292-1214Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chittagong, BangladeshDepartment of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chittagong, BangladeshDepartment of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chittagong, BangladeshSchool of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, CanadaSchool of Engineering, Deakin University, Geelong, VIC, AustraliaTranscranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.https://ieeexplore.ieee.org/document/10312752/Transcranial magnetic stimulationelectric fieldfocalitytransformerMRI imagesregression |
| spellingShingle | Barna Ghosh Khaleda Akhter Sathi Md. Kamal Hosain Md. Azad Hossain M. Ali Akber Dewan Abbas Z. Kouzani ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation IEEE Transactions on Neural Systems and Rehabilitation Engineering Transcranial magnetic stimulation electric field focality transformer MRI images regression |
| title | ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation |
| title_full | ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation |
| title_fullStr | ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation |
| title_full_unstemmed | ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation |
| title_short | ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation |
| title_sort | vitab transformer framework for predicting induced electric field and focality in transcranial magnetic stimulation |
| topic | Transcranial magnetic stimulation electric field focality transformer MRI images regression |
| url | https://ieeexplore.ieee.org/document/10312752/ |
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