Analysis of Induced Field in the Brain Tissue by Transcranial Magnetic Stimulation Using Halo-V Assembly Coil

Sathi, Khaleda Akhter; Hosain, Md. Kamal; Hossain, Md. Azad

doi:10.1155/2022/7424564

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Moreover, the expense of higher and spreading induced electric field at the superficial cortical region can cause serious headaches. By considering this, a halo-V assembly (HVA) coil is developed 14 to provide optimum therapeutic efficacy by ensuring the finest tradeoff at the target side and reducing the expense of stimulation at the non-targeted superficial cortical region. Furthermore, assembly coil has more design parameters than single coil to achieve desired focality-depth tradeoff.…”

Section: Introductionmentioning

confidence: 99%

Attention-assisted hybrid 1D CNN-BiLSTM model for predicting electric field induced by transcranial magnetic stimulation coil

Sathi

Hosain

Hossain

et al. 2023

Sci Rep

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Deep learning-based models such as deep neural network (DNN) and convolutional neural network (CNN) have recently been established as state-of-the-art for enumerating electric fields from transcranial magnetic stimulation coil. One of the main challenges related to this electric field enumeration is the prediction time and accuracy. Despite the low computational cost, the performance of the existing prediction models for electric field enumeration is quite inefficient. This study proposes a 1D CNN-based bi-directional long short-term memory (BiLSTM) model with an attention mechanism to predict electric field induced by a transcranial magnetic stimulation coil. The model employs three consecutive 1D CNN layers followed by the BiLSTM layer for extracting deep features. After that, the weights of the deep features are redistributed and integrated by the attention mechanism and a fully connected layer is utilized for the prediction. For the prediction purpose, six input features including coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil are mapped with the output of the electric field. The performance evaluation is conducted based on four verification metrics (e.g. R2, MSE, MAE, and RMSE) between the simulated data and predicted data. The results indicate that the proposed model outperforms existing DNN and CNN models in predicting the induced electrical field with R2 = 0.9992, MSE = 0.0005, MAE = 0.0188, and RMSE = 0.0228 in the testing stage.

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Section: Introductionmentioning

confidence: 99%

Attention-assisted hybrid 1D CNN-BiLSTM model for predicting electric field induced by transcranial magnetic stimulation coil

Sathi

Hosain

Hossain

et al. 2023

Sci Rep

Self Cite

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“…Pulse shapes determine the physiological impact of TMS, emphasizing the relevance of summarizing the technical specifications of TMS devices. Concerning the shape of the coils, the majority of TMS coils are circular or figure-of-eight shaped, with a range of sizes [ 36 ]. For the purposes of this database, we did not review the shape and models of the coils due to the large number of coils and their varied applications [ 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

The complex landscape of TMS devices: A brief overview

Gutiérrez-Muto,

Bestmann,

Sánchez de la Torre

et al. 2023

PLoS ONE

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The increasing application of TMS in research and therapy has spawned an ever-growing number of commercial and non-commercial TMS devices and technology development. New CE-marked devices appear at a rate of approximately one every two years, with new FDA-approved application of TMS occurring at a similar rate. With the resulting complex landscape of TMS devices and their application, accessible information about the technological characteristics of the TMS devices, such as the type of their circuitry, their pulse characteristics, or permitted protocols would be beneficial. We here present an overview and open access database summarizing key features and applications of available commercial and non-commercial TMS devices (http://www.tmsbase.info). This may guide comparison and decision making about the use of these devices. A bibliometric analysis was performed by identifying commercial and non-commercial TMS devices from which a comprehensive database was created summarizing their publicly available characteristics, both from a technical and clinical point of view. In this document, we introduce both the commercial devices and prototypes found in the literature. The technical specifications that unify these devices are briefly analysed in two separate tables: power electronics, waveform, protocols, and coil types. In the prototype TMS systems, the proposed innovations are focused on improving the treatment regarding the patient: noise cancellation, controllable parameters, and multiple stimulation. This analysis shows that the landscape of TMS is becoming increasingly fragmented, with new devices appearing ever more frequently. The review provided here can support development of benchmarking frameworks and comparison between TMS systems, inform the choice of TMS platforms for specific research and therapeutic applications, and guide future technology development for neuromodulation devices. This standardisation strategy will allow a better end-user choice, with an impact on the TMS manufacturing industry and a homogenisation of patient samples in multi-centre clinical studies. As an open access repository, we envisage the database to grow along with the dynamic development of TMS devices and applications through community-lead curation.

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Enhancement of Penetration Depth and Focality in Transcranial Magnetic Stimulation Using Combined X-halo and Annulus Sector Coils

Cho,

Choi,

Jeong

2024

IEEE Access

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Analysis of Induced Field in the Brain Tissue by Transcranial Magnetic Stimulation Using Halo-V Assembly Coil

Cited by 4 publications

References 27 publications

Attention-assisted hybrid 1D CNN-BiLSTM model for predicting electric field induced by transcranial magnetic stimulation coil

Attention-assisted hybrid 1D CNN-BiLSTM model for predicting electric field induced by transcranial magnetic stimulation coil

The complex landscape of TMS devices: A brief overview

Enhancement of Penetration Depth and Focality in Transcranial Magnetic Stimulation Using Combined X-halo and Annulus Sector Coils

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